{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4511f4b9",
   "metadata": {},
   "source": [
    "# 单独训练一个股票的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "942ef512",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import torch\n",
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "d0117484",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"./../../data/\"\n",
    "OUTPUT_DIR = \"./../../output/\"\n",
    "TEMP_DIR = \"./../../temp/\"\n",
    "MODEL_DIR = \"./../../model\"\n",
    "\n",
    "# 模型参数配置\n",
    "input_size = 1\n",
    "indrnn_hidden_size = 512  # IndRNN隐藏层大小\n",
    "lstm_hidden_size = 256  # LSTM隐藏层大小\n",
    "num_indrnn_layers = 3\n",
    "num_lstm_layers = 1\n",
    "output_size = 1\n",
    "dropout = 0.2  # Dropout比率\n",
    "batch_size = 64\n",
    "num_epochs = 20  # 训练轮数\n",
    "learning_rate = 0.001\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "seq_len = 10  # 序列长度"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e4b4f7",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "6ebaf97f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "股票代码",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "日期",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "开盘",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "收盘",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "最高",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "最低",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "成交量",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "成交额",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "振幅",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "涨跌额",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "换手率",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "涨跌幅",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "1cae36d0-76d7-4b4d-b570-ea9c1bf89718",
       "rows": [
        [
         "0",
         "600000",
         "2015-04-20",
         "9.47",
         "8.89",
         "9.47",
         "8.68",
         "5724358",
         "10446728448.0",
         "8.42",
         "-0.49",
         "3.84",
         "-5.22"
        ],
        [
         "1",
         "600000",
         "2015-04-21",
         "8.79",
         "9.07",
         "9.1",
         "8.79",
         "3681947",
         "6615540736.0",
         "3.49",
         "0.18",
         "2.47",
         "2.02"
        ],
        [
         "2",
         "600000",
         "2015-04-22",
         "9.17",
         "9.31",
         "9.35",
         "9.02",
         "4207667",
         "7712130816.0",
         "3.64",
         "0.24",
         "2.82",
         "2.65"
        ],
        [
         "3",
         "600000",
         "2015-04-23",
         "9.31",
         "9.12",
         "9.41",
         "9.04",
         "3635936",
         "6675542016.0",
         "3.97",
         "-0.19",
         "2.44",
         "-2.04"
        ],
        [
         "4",
         "600000",
         "2015-04-24",
         "8.82",
         "8.74",
         "8.98",
         "8.59",
         "4229271",
         "7509013248.0",
         "4.28",
         "-0.38",
         "2.83",
         "-4.17"
        ]
       ],
       "shape": {
        "columns": 12,
        "rows": 5
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票代码</th>\n",
       "      <th>日期</th>\n",
       "      <th>开盘</th>\n",
       "      <th>收盘</th>\n",
       "      <th>最高</th>\n",
       "      <th>最低</th>\n",
       "      <th>成交量</th>\n",
       "      <th>成交额</th>\n",
       "      <th>振幅</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>换手率</th>\n",
       "      <th>涨跌幅</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600000</td>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>9.47</td>\n",
       "      <td>8.89</td>\n",
       "      <td>9.47</td>\n",
       "      <td>8.68</td>\n",
       "      <td>5724358</td>\n",
       "      <td>1.044673e+10</td>\n",
       "      <td>8.42</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>3.84</td>\n",
       "      <td>-5.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600000</td>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>8.79</td>\n",
       "      <td>9.07</td>\n",
       "      <td>9.10</td>\n",
       "      <td>8.79</td>\n",
       "      <td>3681947</td>\n",
       "      <td>6.615541e+09</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600000</td>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.02</td>\n",
       "      <td>4207667</td>\n",
       "      <td>7.712131e+09</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.82</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600000</td>\n",
       "      <td>2015-04-23</td>\n",
       "      <td>9.31</td>\n",
       "      <td>9.12</td>\n",
       "      <td>9.41</td>\n",
       "      <td>9.04</td>\n",
       "      <td>3635936</td>\n",
       "      <td>6.675542e+09</td>\n",
       "      <td>3.97</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>2.44</td>\n",
       "      <td>-2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600000</td>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.74</td>\n",
       "      <td>8.98</td>\n",
       "      <td>8.59</td>\n",
       "      <td>4229271</td>\n",
       "      <td>7.509013e+09</td>\n",
       "      <td>4.28</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>2.83</td>\n",
       "      <td>-4.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     股票代码          日期    开盘    收盘    最高    最低      成交量           成交额    振幅  \\\n",
       "0  600000  2015-04-20  9.47  8.89  9.47  8.68  5724358  1.044673e+10  8.42   \n",
       "1  600000  2015-04-21  8.79  9.07  9.10  8.79  3681947  6.615541e+09  3.49   \n",
       "2  600000  2015-04-22  9.17  9.31  9.35  9.02  4207667  7.712131e+09  3.64   \n",
       "3  600000  2015-04-23  9.31  9.12  9.41  9.04  3635936  6.675542e+09  3.97   \n",
       "4  600000  2015-04-24  8.82  8.74  8.98  8.59  4229271  7.509013e+09  4.28   \n",
       "\n",
       "    涨跌额   换手率   涨跌幅  \n",
       "0 -0.49  3.84 -5.22  \n",
       "1  0.18  2.47  2.02  \n",
       "2  0.24  2.82  2.65  \n",
       "3 -0.19  2.44 -2.04  \n",
       "4 -0.38  2.83 -4.17  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(os.path.join(DATA_DIR, \"train.csv\"))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "87d77eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"股票代码\"].unique().__len__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "4bc93482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(os.path.join(DATA_DIR, \"test.csv\"))[\"股票代码\"].unique().__len__()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aecf259c",
   "metadata": {},
   "source": [
    "选取其中一个股票进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "84236ad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机选择的股票代码: 600519\n"
     ]
    }
   ],
   "source": [
    "# stock = 661 or random.choice(data[\"股票代码\"].unique())\n",
    "stock = 600519 or random.choice(data[\"股票代码\"].unique())\n",
    "print(\"随机选择的股票代码:\", stock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "8af5e23e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data[data[\"股票代码\"] == stock].copy()\n",
    "df = df.drop(columns=[\"股票代码\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "3dddaadc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Date",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "Open",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Close",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "High",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Low",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Volume",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "Turnover",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Amplitude",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PriceChange",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "TurnoverRate",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "PriceChangePercentage",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "9acc9dc7-9920-4743-8c45-eb8337da5f77",
       "rows": [
        [
         "123670",
         "2015-04-20",
         "-13.06",
         "-9.4",
         "1.21",
         "-13.97",
         "130589",
         "3118764544.0",
         "-141.47",
         "1.33",
         "1.14",
         "12.4"
        ],
        [
         "123671",
         "2015-04-21",
         "-3.97",
         "11.88",
         "11.88",
         "-3.97",
         "122402",
         "3039243968.0",
         "-168.62",
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         "226.38"
        ],
        [
         "123672",
         "2015-04-22",
         "22.21",
         "17.75",
         "22.4",
         "11.59",
         "134635",
         "3543576704.0",
         "90.99",
         "5.87",
         "1.18",
         "49.41"
        ]
       ],
       "shape": {
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        "rows": 3
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      },
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>PriceChange</th>\n",
       "      <th>TurnoverRate</th>\n",
       "      <th>PriceChangePercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123670</th>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>-13.06</td>\n",
       "      <td>-9.40</td>\n",
       "      <td>1.21</td>\n",
       "      <td>-13.97</td>\n",
       "      <td>130589</td>\n",
       "      <td>3.118765e+09</td>\n",
       "      <td>-141.47</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.14</td>\n",
       "      <td>12.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123671</th>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>-3.97</td>\n",
       "      <td>11.88</td>\n",
       "      <td>11.88</td>\n",
       "      <td>-3.97</td>\n",
       "      <td>122402</td>\n",
       "      <td>3.039244e+09</td>\n",
       "      <td>-168.62</td>\n",
       "      <td>21.28</td>\n",
       "      <td>1.07</td>\n",
       "      <td>226.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123672</th>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>22.21</td>\n",
       "      <td>17.75</td>\n",
       "      <td>22.40</td>\n",
       "      <td>11.59</td>\n",
       "      <td>134635</td>\n",
       "      <td>3.543577e+09</td>\n",
       "      <td>90.99</td>\n",
       "      <td>5.87</td>\n",
       "      <td>1.18</td>\n",
       "      <td>49.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date   Open  Close   High    Low  Volume      Turnover  \\\n",
       "123670  2015-04-20 -13.06  -9.40   1.21 -13.97  130589  3.118765e+09   \n",
       "123671  2015-04-21  -3.97  11.88  11.88  -3.97  122402  3.039244e+09   \n",
       "123672  2015-04-22  22.21  17.75  22.40  11.59  134635  3.543577e+09   \n",
       "\n",
       "        Amplitude  PriceChange  TurnoverRate  PriceChangePercentage  \n",
       "123670    -141.47         1.33          1.14                  12.40  \n",
       "123671    -168.62        21.28          1.07                 226.38  \n",
       "123672      90.99         5.87          1.18                  49.41  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_mapping = {\n",
    "    \"股票代码\": \"StockCode\",\n",
    "    \"日期\": \"Date\",\n",
    "    \"开盘\": \"Open\",\n",
    "    \"收盘\": \"Close\",\n",
    "    \"最高\": \"High\",\n",
    "    \"最低\": \"Low\",\n",
    "    \"成交量\": \"Volume\",\n",
    "    \"成交额\": \"Turnover\",\n",
    "    \"振幅\": \"Amplitude\",\n",
    "    \"涨跌额\": \"PriceChange\",\n",
    "    \"换手率\": \"TurnoverRate\",\n",
    "    \"涨跌幅\": \"PriceChangePercentage\",\n",
    "}\n",
    "\n",
    "df.rename(columns=column_mapping, inplace=True)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "4a3f0aaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "发现异常值的特征数量: 6\n",
      "Amplitude: 346 个异常值 (14.23%)\n",
      "PriceChange: 292 个异常值 (12.01%)\n",
      "PriceChangePercentage: 314 个异常值 (12.91%)\n",
      "Turnover: 94 个异常值 (3.87%)\n",
      "TurnoverRate: 137 个异常值 (5.63%)\n",
      "Volume: 141 个异常值 (5.80%)\n"
     ]
    }
   ],
   "source": [
    "# 检查数据，查找异常值\n",
    "def identify_outliers(df, columns):\n",
    "    \"\"\"\n",
    "    识别并返回指定列的异常值信息\n",
    "\n",
    "    Args:\n",
    "        df: DataFrame\n",
    "        columns: 要检查的列名列表\n",
    "\n",
    "    Returns:\n",
    "        异常值信息的DataFrame\n",
    "    \"\"\"\n",
    "    outliers_info = {}\n",
    "\n",
    "    for col in columns:\n",
    "        if col in df.columns and col != \"Date\":\n",
    "            Q1 = df[col].quantile(0.25)\n",
    "            Q3 = df[col].quantile(0.75)\n",
    "            IQR = Q3 - Q1\n",
    "\n",
    "            lower_bound = Q1 - 1.5 * IQR\n",
    "            upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "            outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "            if not outliers.empty:\n",
    "                pct = len(outliers) / len(df) * 100\n",
    "                outliers_info[col] = {\n",
    "                    \"count\": len(outliers),\n",
    "                    \"percentage\": pct,\n",
    "                    \"min\": df[col].min(),\n",
    "                    \"max\": df[col].max(),\n",
    "                    \"lower_bound\": lower_bound,\n",
    "                    \"upper_bound\": upper_bound,\n",
    "                }\n",
    "\n",
    "    return outliers_info\n",
    "\n",
    "\n",
    "# 处理异常值的函数\n",
    "def handle_outliers(df, columns, method=\"winsorize\"):\n",
    "    \"\"\"\n",
    "    处理异常值\n",
    "\n",
    "    Args:\n",
    "        df: DataFrame\n",
    "        columns: 要处理的列名列表\n",
    "        method: 处理方法 ('winsorize', 'trim', 'mean', 'median')\n",
    "\n",
    "    Returns:\n",
    "        处理后的DataFrame副本\n",
    "    \"\"\"\n",
    "    df_cleaned = df.copy()\n",
    "\n",
    "    for col in columns:\n",
    "        if col in df.columns and col != \"Date\" and col != \"Close\":  # 保留Close原始值\n",
    "            Q1 = df[col].quantile(0.25)\n",
    "            Q3 = df[col].quantile(0.75)\n",
    "            IQR = Q3 - Q1\n",
    "\n",
    "            lower_bound = Q1 - 1.5 * IQR\n",
    "            upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "            if method == \"winsorize\":\n",
    "                # 极端值替换为边界值\n",
    "                df_cleaned[col] = df_cleaned[col].clip(lower_bound, upper_bound)\n",
    "            elif method == \"trim\":\n",
    "                # 移除异常值行（不推荐用于时间序列）\n",
    "                df_cleaned = df_cleaned[\n",
    "                    (df_cleaned[col] >= lower_bound) & (df_cleaned[col] <= upper_bound)\n",
    "                ]\n",
    "            elif method == \"mean\":\n",
    "                # 用均值替换\n",
    "                mask = (df_cleaned[col] < lower_bound) | (df_cleaned[col] > upper_bound)\n",
    "                df_cleaned.loc[mask, col] = df_cleaned[col].mean()\n",
    "            elif method == \"median\":\n",
    "                # 用中位数替换\n",
    "                mask = (df_cleaned[col] < lower_bound) | (df_cleaned[col] > upper_bound)\n",
    "                df_cleaned.loc[mask, col] = df_cleaned[col].median()\n",
    "\n",
    "    return df_cleaned\n",
    "\n",
    "\n",
    "# 先检查哪些特征存在异常值\n",
    "features = df.columns.difference([\"Date\"])  # 排除Date和Close\n",
    "features_to_check = [f for f in features if f != \"Close\"]  # 排除Close\n",
    "outliers_info = identify_outliers(df, features_to_check)\n",
    "\n",
    "# 输出异常值信息\n",
    "print(f\"发现异常值的特征数量: {len(outliers_info)}\")\n",
    "for col, info in outliers_info.items():\n",
    "    print(f\"{col}: {info['count']} 个异常值 ({info['percentage']:.2f}%)\")\n",
    "\n",
    "# 处理异常值\n",
    "df_cleaned = handle_outliers(df, features_to_check, method=\"winsorize\")\n",
    "\n",
    "\n",
    "# 使用处理后的数据替换原始数据框\n",
    "df = df_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66ef8da",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "b4fe4939",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "添加技术指标后的特征数量: 24\n",
      "新增技术指标列: ['MA.MA1', 'MA.MA2', 'MA.MA3', 'MA.MA4', 'MA.MA5', 'MA.MA6', 'KDJ.K', 'KDJ.D', 'KDJ.J', 'MACD.DIFF', 'MACD.DEA', 'MACD.MACD', 'CCI.CCI']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xiaof\\AppData\\Local\\Temp\\ipykernel_6668\\2791560740.py:63: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  df.fillna(method=\"bfill\", inplace=True)\n",
      "C:\\Users\\xiaof\\AppData\\Local\\Temp\\ipykernel_6668\\2791560740.py:64: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
      "  df.fillna(method=\"ffill\", inplace=True)\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>PriceChange</th>\n",
       "      <th>TurnoverRate</th>\n",
       "      <th>...</th>\n",
       "      <th>MA.MA4</th>\n",
       "      <th>MA.MA5</th>\n",
       "      <th>MA.MA6</th>\n",
       "      <th>KDJ.K</th>\n",
       "      <th>KDJ.D</th>\n",
       "      <th>KDJ.J</th>\n",
       "      <th>MACD.DIFF</th>\n",
       "      <th>MACD.DEA</th>\n",
       "      <th>MACD.MACD</th>\n",
       "      <th>CCI.CCI</th>\n",
       "    </tr>\n",
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       "      <th>123670</th>\n",
       "      <td>2015-04-20</td>\n",
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       "      <td>3.118765e+09</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.59</td>\n",
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       "      <td>10.422667</td>\n",
       "      <td>-3.04575</td>\n",
       "      <td>30.105402</td>\n",
       "      <td>30.105402</td>\n",
       "      <td>30.105402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>-3.97</td>\n",
       "      <td>73762.625</td>\n",
       "      <td>3.039244e+09</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>21.28</td>\n",
       "      <td>0.59</td>\n",
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       "      <th>123672</th>\n",
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       "      <td>11.59</td>\n",
       "      <td>73762.625</td>\n",
       "      <td>3.543577e+09</td>\n",
       "      <td>7.65</td>\n",
       "      <td>5.87</td>\n",
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       "      <td>...</td>\n",
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       "      <td>10.422667</td>\n",
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       "      <td>64.673980</td>\n",
       "      <td>46.805639</td>\n",
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       "      <td>3.476456</td>\n",
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       "      <td>91.368148</td>\n",
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       "<p>3 rows × 24 columns</p>\n",
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      "text/plain": [
       "              Date   Open  Close   High    Low     Volume      Turnover  \\\n",
       "123670  2015-04-20 -13.06  -9.40   1.21 -13.97  73762.625  3.118765e+09   \n",
       "123671  2015-04-21  -3.97  11.88  11.88  -3.97  73762.625  3.039244e+09   \n",
       "123672  2015-04-22  22.21  17.75  22.40  11.59  73762.625  3.543577e+09   \n",
       "\n",
       "        Amplitude  PriceChange  TurnoverRate  ...     MA.MA4     MA.MA5  \\\n",
       "123670      -1.87         1.33          0.59  ...  11.284333  10.422667   \n",
       "123671      -1.87        21.28          0.59  ...  11.284333  10.422667   \n",
       "123672       7.65         5.87          0.59  ...  11.284333  10.422667   \n",
       "\n",
       "         MA.MA6      KDJ.K      KDJ.D       KDJ.J  MACD.DIFF  MACD.DEA  \\\n",
       "123670 -3.04575  30.105402  30.105402   30.105402   0.000000  0.000000   \n",
       "123671 -3.04575  53.403601  37.871468   84.467867   1.697550  0.339510   \n",
       "123672 -3.04575  64.673980  46.805639  100.410662   3.476456  0.966899   \n",
       "\n",
       "        MACD.MACD    CCI.CCI  \n",
       "123670   0.000000  66.666667  \n",
       "123671   2.716080  66.666667  \n",
       "123672   5.019113  91.368148  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加技术指标特征\n",
    "# 计算移动平均线 (MA)\n",
    "df[\"MA.MA1\"] = df[\"Close\"].rolling(window=5).mean()\n",
    "df[\"MA.MA2\"] = df[\"Close\"].rolling(window=10).mean()\n",
    "df[\"MA.MA3\"] = df[\"Close\"].rolling(window=20).mean()\n",
    "df[\"MA.MA4\"] = df[\"Close\"].rolling(window=30).mean()\n",
    "df[\"MA.MA5\"] = df[\"Close\"].rolling(window=60).mean()\n",
    "df[\"MA.MA6\"] = df[\"Close\"].rolling(window=120).mean()\n",
    "\n",
    "\n",
    "# 计算KDJ指标\n",
    "def calculate_kdj(data, n=10, m1=3, m2=3):\n",
    "    data = data.copy()\n",
    "    low_list = data[\"Low\"].rolling(window=n, min_periods=1).min()\n",
    "    high_list = data[\"High\"].rolling(window=n, min_periods=1).max()\n",
    "\n",
    "    rsv = (data[\"Close\"] - low_list) / (high_list - low_list) * 100\n",
    "    data[\"KDJ.K\"] = rsv.ewm(alpha=1 / m1, adjust=False).mean()\n",
    "    data[\"KDJ.D\"] = data[\"KDJ.K\"].ewm(alpha=1 / m2, adjust=False).mean()\n",
    "    data[\"KDJ.J\"] = 3 * data[\"KDJ.K\"] - 2 * data[\"KDJ.D\"]\n",
    "    return data\n",
    "\n",
    "\n",
    "# 计算KDJ\n",
    "kdj_data = calculate_kdj(df)\n",
    "df[[\"KDJ.K\", \"KDJ.D\", \"KDJ.J\"]] = kdj_data[[\"KDJ.K\", \"KDJ.D\", \"KDJ.J\"]]\n",
    "\n",
    "\n",
    "# 计算MACD指标\n",
    "def calculate_macd(data, short_window=12, long_window=26, signal_window=9):\n",
    "    data = data.copy()\n",
    "    data[\"MACD.DIFF\"] = (\n",
    "        data[\"Close\"].ewm(span=short_window, adjust=False).mean()\n",
    "        - data[\"Close\"].ewm(span=long_window, adjust=False).mean()\n",
    "    )\n",
    "    data[\"MACD.DEA\"] = data[\"MACD.DIFF\"].ewm(span=signal_window, adjust=False).mean()\n",
    "    data[\"MACD.MACD\"] = 2 * (data[\"MACD.DIFF\"] - data[\"MACD.DEA\"])\n",
    "    return data\n",
    "\n",
    "\n",
    "# 计算MACD\n",
    "macd_data = calculate_macd(df)\n",
    "df[[\"MACD.DIFF\", \"MACD.DEA\", \"MACD.MACD\"]] = macd_data[\n",
    "    [\"MACD.DIFF\", \"MACD.DEA\", \"MACD.MACD\"]\n",
    "]\n",
    "\n",
    "\n",
    "# 计算CCI指标\n",
    "def calculate_cci(data, n=10):\n",
    "    data = data.copy()\n",
    "    tp = (data[\"High\"] + data[\"Low\"] + data[\"Close\"]) / 3\n",
    "    ma = tp.rolling(window=n, min_periods=1).mean()\n",
    "    md = tp.rolling(window=n, min_periods=1).apply(lambda x: abs(x - x.mean()).mean())\n",
    "    data[\"CCI.CCI\"] = (tp - ma) / (0.015 * md)\n",
    "    return data\n",
    "\n",
    "\n",
    "# 计算CCI\n",
    "cci_data = calculate_cci(df)\n",
    "df[\"CCI.CCI\"] = cci_data[\"CCI.CCI\"]\n",
    "\n",
    "# 填充NaN值\n",
    "df.fillna(method=\"bfill\", inplace=True)\n",
    "df.fillna(method=\"ffill\", inplace=True)\n",
    "df.fillna(0, inplace=True)\n",
    "\n",
    "# 显示添加技术指标后的数据预览\n",
    "print(\"添加技术指标后的特征数量:\", len(df.columns))\n",
    "print(\"新增技术指标列:\", df.columns[-13:].tolist())\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c657442c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "15b8cb06",
   "metadata": {},
   "source": [
    "标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "dc3e500b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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        {
         "name": "Low",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Volume",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "Turnover",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "Amplitude",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "PriceChange",
         "rawType": "float64",
         "type": "float"
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         "name": "TurnoverRate",
         "rawType": "float64",
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        {
         "name": "PriceChangePercentage",
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         "type": "float"
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         "rawType": "float64",
         "type": "float"
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         "name": "KDJ.J",
         "rawType": "float64",
         "type": "float"
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        {
         "name": "MACD.DIFF",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "MACD.DEA",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "MACD.MACD",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "CCI.CCI",
         "rawType": "float64",
         "type": "float"
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       ],
       "shape": {
        "columns": 24,
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      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>PriceChange</th>\n",
       "      <th>TurnoverRate</th>\n",
       "      <th>...</th>\n",
       "      <th>MA.MA4</th>\n",
       "      <th>MA.MA5</th>\n",
       "      <th>MA.MA6</th>\n",
       "      <th>KDJ.K</th>\n",
       "      <th>KDJ.D</th>\n",
       "      <th>KDJ.J</th>\n",
       "      <th>MACD.DIFF</th>\n",
       "      <th>MACD.DEA</th>\n",
       "      <th>MACD.MACD</th>\n",
       "      <th>CCI.CCI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123670</th>\n",
       "      <td>2015-04-20</td>\n",
       "      <td>-1.480017</td>\n",
       "      <td>-9.40</td>\n",
       "      <td>-1.464540</td>\n",
       "      <td>-1.478222</td>\n",
       "      <td>2.400328</td>\n",
       "      <td>-0.312579</td>\n",
       "      <td>-2.170382</td>\n",
       "      <td>0.047776</td>\n",
       "      <td>2.391313</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.431216</td>\n",
       "      <td>-1.41652</td>\n",
       "      <td>-1.402126</td>\n",
       "      <td>-1.032192</td>\n",
       "      <td>-1.163363</td>\n",
       "      <td>-0.683792</td>\n",
       "      <td>-0.173082</td>\n",
       "      <td>-0.184634</td>\n",
       "      <td>-0.001464</td>\n",
       "      <td>0.527592</td>\n",
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       "    <tr>\n",
       "      <th>123671</th>\n",
       "      <td>2015-04-21</td>\n",
       "      <td>-1.466736</td>\n",
       "      <td>11.88</td>\n",
       "      <td>-1.449113</td>\n",
       "      <td>-1.463455</td>\n",
       "      <td>2.400328</td>\n",
       "      <td>-0.343320</td>\n",
       "      <td>-2.170382</td>\n",
       "      <td>1.212678</td>\n",
       "      <td>2.391313</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.431216</td>\n",
       "      <td>-1.41652</td>\n",
       "      <td>-1.402126</td>\n",
       "      <td>-0.020762</td>\n",
       "      <td>-0.782823</td>\n",
       "      <td>0.875108</td>\n",
       "      <td>-0.107597</td>\n",
       "      <td>-0.170626</td>\n",
       "      <td>0.163568</td>\n",
       "      <td>0.527592</td>\n",
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       "    <tr>\n",
       "      <th>123672</th>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>-1.428488</td>\n",
       "      <td>17.75</td>\n",
       "      <td>-1.433903</td>\n",
       "      <td>-1.440477</td>\n",
       "      <td>2.400328</td>\n",
       "      <td>-0.148354</td>\n",
       "      <td>2.052192</td>\n",
       "      <td>0.312872</td>\n",
       "      <td>2.391313</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.431216</td>\n",
       "      <td>-1.41652</td>\n",
       "      <td>-1.402126</td>\n",
       "      <td>0.468512</td>\n",
       "      <td>-0.345046</td>\n",
       "      <td>1.332284</td>\n",
       "      <td>-0.038973</td>\n",
       "      <td>-0.144738</td>\n",
       "      <td>0.303503</td>\n",
       "      <td>0.764287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123673</th>\n",
       "      <td>2015-04-23</td>\n",
       "      <td>-1.434858</td>\n",
       "      <td>10.61</td>\n",
       "      <td>-1.440265</td>\n",
       "      <td>-1.446029</td>\n",
       "      <td>2.400328</td>\n",
       "      <td>-0.662060</td>\n",
       "      <td>2.052192</td>\n",
       "      <td>-0.446796</td>\n",
       "      <td>2.391313</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.431216</td>\n",
       "      <td>-1.41652</td>\n",
       "      <td>-1.402126</td>\n",
       "      <td>0.510611</td>\n",
       "      <td>-0.037356</td>\n",
       "      <td>1.055574</td>\n",
       "      <td>-0.008709</td>\n",
       "      <td>-0.117554</td>\n",
       "      <td>0.318781</td>\n",
       "      <td>0.311714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123674</th>\n",
       "      <td>2015-04-24</td>\n",
       "      <td>-1.450958</td>\n",
       "      <td>12.84</td>\n",
       "      <td>-1.446164</td>\n",
       "      <td>-1.456721</td>\n",
       "      <td>2.400328</td>\n",
       "      <td>-0.533459</td>\n",
       "      <td>2.052192</td>\n",
       "      <td>0.100328</td>\n",
       "      <td>2.391313</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.431216</td>\n",
       "      <td>-1.41652</td>\n",
       "      <td>-1.402126</td>\n",
       "      <td>0.627403</td>\n",
       "      <td>0.211713</td>\n",
       "      <td>0.995494</td>\n",
       "      <td>0.019992</td>\n",
       "      <td>-0.089667</td>\n",
       "      <td>0.327062</td>\n",
       "      <td>0.046958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date      Open  Close      High       Low    Volume  Turnover  \\\n",
       "123670  2015-04-20 -1.480017  -9.40 -1.464540 -1.478222  2.400328 -0.312579   \n",
       "123671  2015-04-21 -1.466736  11.88 -1.449113 -1.463455  2.400328 -0.343320   \n",
       "123672  2015-04-22 -1.428488  17.75 -1.433903 -1.440477  2.400328 -0.148354   \n",
       "123673  2015-04-23 -1.434858  10.61 -1.440265 -1.446029  2.400328 -0.662060   \n",
       "123674  2015-04-24 -1.450958  12.84 -1.446164 -1.456721  2.400328 -0.533459   \n",
       "\n",
       "        Amplitude  PriceChange  TurnoverRate  ...    MA.MA4   MA.MA5  \\\n",
       "123670  -2.170382     0.047776      2.391313  ... -1.431216 -1.41652   \n",
       "123671  -2.170382     1.212678      2.391313  ... -1.431216 -1.41652   \n",
       "123672   2.052192     0.312872      2.391313  ... -1.431216 -1.41652   \n",
       "123673   2.052192    -0.446796      2.391313  ... -1.431216 -1.41652   \n",
       "123674   2.052192     0.100328      2.391313  ... -1.431216 -1.41652   \n",
       "\n",
       "          MA.MA6     KDJ.K     KDJ.D     KDJ.J  MACD.DIFF  MACD.DEA  \\\n",
       "123670 -1.402126 -1.032192 -1.163363 -0.683792  -0.173082 -0.184634   \n",
       "123671 -1.402126 -0.020762 -0.782823  0.875108  -0.107597 -0.170626   \n",
       "123672 -1.402126  0.468512 -0.345046  1.332284  -0.038973 -0.144738   \n",
       "123673 -1.402126  0.510611 -0.037356  1.055574  -0.008709 -0.117554   \n",
       "123674 -1.402126  0.627403  0.211713  0.995494   0.019992 -0.089667   \n",
       "\n",
       "        MACD.MACD   CCI.CCI  \n",
       "123670  -0.001464  0.527592  \n",
       "123671   0.163568  0.527592  \n",
       "123672   0.303503  0.764287  \n",
       "123673   0.318781  0.311714  \n",
       "123674   0.327062  0.046958  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df.columns.difference([\"Date\"]).tolist()\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "backup_close = df[\"Close\"].copy()\n",
    "df[features] = scaler.fit_transform(df[features])\n",
    "df[\"Close\"] = backup_close\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "fe162a9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class StockDataset(Dataset):\n",
    "    def __init__(self, df, seq_length, features):\n",
    "        self.df = df\n",
    "        self.seq_length = seq_length\n",
    "        self.features = features\n",
    "\n",
    "        if \"Date\" in df.columns:\n",
    "            self.df = self.df.sort_values(\"Date\").reset_index(drop=True)\n",
    "\n",
    "        self.data = self.df[features].values\n",
    "        self.targets = self.df[\"Close\"].values\n",
    "\n",
    "        total_samples = len(self.df) - seq_length\n",
    "        train_size = int(total_samples)\n",
    "        self.start_idx = 0\n",
    "        self.end_idx = train_size\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.end_idx - self.start_idx\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        actual_idx = self.start_idx + idx\n",
    "\n",
    "        X = self.data[actual_idx : actual_idx + self.seq_length]\n",
    "        y = self.targets[actual_idx + self.seq_length]\n",
    "\n",
    "        X = torch.tensor(X, dtype=torch.float32)\n",
    "        y = torch.tensor(y, dtype=torch.float32)\n",
    "\n",
    "        return X, y\n",
    "\n",
    "\n",
    "train_dataset = StockDataset(df, seq_len, features)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e157d3",
   "metadata": {},
   "source": [
    "## 模型构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "8ec533f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_size(model):\n",
    "    \"\"\"\n",
    "    计算模型的参数总量\n",
    "    Args:\n",
    "        model: PyTorch模型\n",
    "    Returns:\n",
    "        size: 模型参数总量（单位：MB）\n",
    "    \"\"\"\n",
    "    total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "    size = total_params * 4 / (1024**2)  # 转换为MB\n",
    "    return size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c517be7a",
   "metadata": {},
   "source": [
    "### IndRNN-LSTM模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "30528a5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数总量: 5.07 MB\n",
      "模型结构:\n",
      "IndRNNLSTMModel(\n",
      "  (indrnn): IndRNN(\n",
      "    (cells): ModuleList(\n",
      "      (0-2): 3 x IndRNNCell()\n",
      "    )\n",
      "    (dropout_layer): Dropout(p=0.2, inplace=False)\n",
      "  )\n",
      "  (lstm): LSTM(512, 256, batch_first=True)\n",
      "  (dropout): Dropout(p=0.2, inplace=False)\n",
      "  (fc): Linear(in_features=256, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class IndRNNCell(nn.Module):\n",
    "    \"\"\"\n",
    "    Independent RNN Cell\n",
    "    IndRNN的核心思想是每个隐藏单元只依赖于自身的前一时刻状态和当前输入\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, input_size, hidden_size):\n",
    "        super(IndRNNCell, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        # 输入到隐藏层的权重\n",
    "        self.weight_ih = nn.Parameter(torch.randn(hidden_size, input_size))\n",
    "        # 隐藏状态的递归权重（对角矩阵，每个神经元独立）\n",
    "        self.weight_hh = nn.Parameter(torch.randn(hidden_size))\n",
    "        # 偏置\n",
    "        self.bias = nn.Parameter(torch.randn(hidden_size))\n",
    "\n",
    "        self.reset_parameters()\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        \"\"\"初始化参数\"\"\"\n",
    "        std = 1.0 / np.sqrt(self.hidden_size)\n",
    "        for weight in self.parameters():\n",
    "            weight.data.uniform_(-std, std)\n",
    "\n",
    "    def forward(self, input, hidden):\n",
    "        \"\"\"\n",
    "        前向传播\n",
    "        Args:\n",
    "            input: (batch_size, input_size)\n",
    "            hidden: (batch_size, hidden_size)\n",
    "        Returns:\n",
    "            new_hidden: (batch_size, hidden_size)\n",
    "        \"\"\"\n",
    "        # 计算输入部分：W_ih * x_t\n",
    "        input_part = torch.mm(input, self.weight_ih.t())\n",
    "\n",
    "        # 计算隐藏状态部分：W_hh * h_{t-1} (element-wise multiplication)\n",
    "        hidden_part = hidden * self.weight_hh\n",
    "\n",
    "        # 计算新的隐藏状态：ReLU(W_ih * x_t + W_hh * h_{t-1} + b) 替换原来的tanh\n",
    "        new_hidden = torch.relu(input_part + hidden_part + self.bias)\n",
    "\n",
    "        return new_hidden\n",
    "\n",
    "\n",
    "class IndRNN(nn.Module):\n",
    "    \"\"\"\n",
    "    Independent RNN层\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, input_size, hidden_size, num_layers=1, dropout=0.0):\n",
    "        super(IndRNN, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        self.dropout = dropout\n",
    "\n",
    "        # 创建多层IndRNN\n",
    "        self.cells = nn.ModuleList()\n",
    "        for i in range(num_layers):\n",
    "            layer_input_size = input_size if i == 0 else hidden_size\n",
    "            self.cells.append(IndRNNCell(layer_input_size, hidden_size))\n",
    "\n",
    "        # Dropout层\n",
    "        if dropout > 0:\n",
    "            self.dropout_layer = nn.Dropout(dropout)\n",
    "        else:\n",
    "            self.dropout_layer = None\n",
    "\n",
    "    def forward(self, input, hidden=None):\n",
    "        \"\"\"\n",
    "        前向传播\n",
    "        Args:\n",
    "            input: (batch_size, seq_len, input_size)\n",
    "            hidden: 初始隐藏状态\n",
    "        Returns:\n",
    "            output: (batch_size, seq_len, hidden_size)\n",
    "            hidden: 最终隐藏状态\n",
    "        \"\"\"\n",
    "        batch_size, seq_len, _ = input.size()\n",
    "\n",
    "        if hidden is None:\n",
    "            hidden = [\n",
    "                torch.zeros(batch_size, self.hidden_size).to(input.device)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "\n",
    "        outputs = []\n",
    "\n",
    "        for t in range(seq_len):\n",
    "            x = input[:, t, :]\n",
    "\n",
    "            for layer in range(self.num_layers):\n",
    "                x = self.cells[layer](x, hidden[layer])\n",
    "                hidden[layer] = x\n",
    "\n",
    "                # 应用dropout（除了最后一层）\n",
    "                if self.dropout_layer is not None and layer < self.num_layers - 1:\n",
    "                    x = self.dropout_layer(x)\n",
    "\n",
    "            outputs.append(x)\n",
    "\n",
    "        # 堆叠输出\n",
    "        output = torch.stack(outputs, dim=1)\n",
    "\n",
    "        return output, hidden\n",
    "\n",
    "\n",
    "class IndRNNLSTMModel(nn.Module):\n",
    "    \"\"\"\n",
    "    IndRNN-LSTM混合模型\n",
    "    先使用IndRNN处理输入序列，然后使用LSTM进行进一步的时序建模\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        input_size,\n",
    "        indrnn_hidden_size,\n",
    "        lstm_hidden_size,\n",
    "        num_indrnn_layers=1,\n",
    "        num_lstm_layers=1,\n",
    "        output_size=1,\n",
    "        dropout=0.2,\n",
    "    ):\n",
    "        super(IndRNNLSTMModel, self).__init__()\n",
    "\n",
    "        self.input_size = input_size\n",
    "        self.indrnn_hidden_size = indrnn_hidden_size\n",
    "        self.lstm_hidden_size = lstm_hidden_size\n",
    "        self.num_indrnn_layers = num_indrnn_layers\n",
    "        self.num_lstm_layers = num_lstm_layers\n",
    "        self.output_size = output_size\n",
    "\n",
    "        # IndRNN层\n",
    "        self.indrnn = IndRNN(\n",
    "            input_size=input_size,\n",
    "            hidden_size=indrnn_hidden_size,\n",
    "            num_layers=num_indrnn_layers,\n",
    "            dropout=dropout,\n",
    "        )\n",
    "\n",
    "        # LSTM层\n",
    "        self.lstm = nn.LSTM(\n",
    "            input_size=indrnn_hidden_size,\n",
    "            hidden_size=lstm_hidden_size,\n",
    "            num_layers=num_lstm_layers,\n",
    "            batch_first=True,  # 保证输入形状为 (batch_size, seq_len, input_size)\n",
    "            dropout=dropout if num_lstm_layers > 1 else 0,\n",
    "        )\n",
    "\n",
    "        # Dropout层\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "        # 输出层\n",
    "        self.fc = nn.Linear(lstm_hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        前向传播\n",
    "        Args:\n",
    "            x: (batch_size, seq_len, input_size)\n",
    "        Returns:\n",
    "            output: (batch_size, output_size)\n",
    "        \"\"\"\n",
    "        # IndRNN处理\n",
    "        indrnn_out, _ = self.indrnn(x)\n",
    "\n",
    "        # LSTM处理\n",
    "        lstm_out, _ = self.lstm(indrnn_out)\n",
    "\n",
    "        # 取最后一个时间步的输出\n",
    "        last_output = lstm_out[:, -1, :]\n",
    "\n",
    "        # Dropout\n",
    "        last_output = self.dropout(last_output)\n",
    "\n",
    "        # 全连接层输出\n",
    "        output = self.fc(last_output)\n",
    "\n",
    "        return output\n",
    "\n",
    "\n",
    "# 创建模型实例\n",
    "model = IndRNNLSTMModel(\n",
    "    input_size=len(features),  # 输入特征维度\n",
    "    indrnn_hidden_size=indrnn_hidden_size,\n",
    "    lstm_hidden_size=lstm_hidden_size,\n",
    "    num_indrnn_layers=num_indrnn_layers,\n",
    "    num_lstm_layers=num_lstm_layers,\n",
    "    output_size=output_size,\n",
    "    dropout=dropout,\n",
    ")\n",
    "\n",
    "model.to(device)\n",
    "\n",
    "# 输出模型大小\n",
    "print(f\"模型参数总量: {model_size(model):.2f} MB\")\n",
    "print(f\"模型结构:\")\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "3ea87693",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 1/20, Train Loss: 1452264.9658\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 2/20, Train Loss: 1436564.7413\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 3/20, Train Loss: 1422510.7014\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 4/20, Train Loss: 1410634.3326\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 5/20, Train Loss: 1399891.8669\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 6/20, Train Loss: 1393463.2133\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 7/20, Train Loss: 1391679.9005\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 8/20, Train Loss: 1383482.6865\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 9/20, Train Loss: 1373927.2400\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 10/20, Train Loss: 1369667.9474\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 11/20, Train Loss: 1360384.7416\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 12/20, Train Loss: 1356301.6753\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 13/20, Train Loss: 1347044.2320\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 14/20, Train Loss: 1335966.2165\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 15/20, Train Loss: 1325268.8813\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 16/20, Train Loss: 1314055.9297\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 17/20, Train Loss: 1303481.0551\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 18/20, Train Loss: 1293624.9546\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 19/20, Train Loss: 1283357.3060\n",
      "保存最佳模型到 ./../../model\\best_indrnn_lstm_model_600519.pth\n",
      "Epoch 20/20, Train Loss: 1277861.8834\n"
     ]
    }
   ],
   "source": [
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "criterion_mse = nn.MSELoss()\n",
    "best_train_loss = float(\"inf\")\n",
    "best_model_path = os.path.join(MODEL_DIR, f\"best_indrnn_lstm_model_{stock}.pth\")\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    for X_batch, y_batch in train_loader:\n",
    "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(X_batch)\n",
    "        loss = criterion_mse(outputs.squeeze(), y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item() * X_batch.size(0)\n",
    "\n",
    "    train_loss /= len(train_dataset)\n",
    "\n",
    "    # 保存最佳模型\n",
    "    if train_loss < best_train_loss:\n",
    "        best_train_loss = train_loss\n",
    "        torch.save(model.state_dict(), best_model_path)\n",
    "        print(f\"保存最佳模型到 {best_model_path}\")\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "9526b670",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xiaof\\AppData\\Local\\Temp\\ipykernel_6668\\3475038031.py:6: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(best_model_path))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "\n",
    "\n",
    "# 加载最佳模型\n",
    "model.load_state_dict(torch.load(best_model_path))\n",
    "model.eval()\n",
    "\n",
    "# 准备所有数据进行预测\n",
    "all_dataset = StockDataset(df, seq_len, features)\n",
    "all_loader = DataLoader(all_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "# 进行预测\n",
    "predictions = []\n",
    "actuals = []\n",
    "with torch.no_grad():\n",
    "    for X_batch, y_batch in all_loader:\n",
    "        X_batch = X_batch.to(device)\n",
    "        output = model(X_batch)\n",
    "        predictions.extend(output.cpu().numpy().flatten())\n",
    "        actuals.extend(y_batch.numpy())\n",
    "\n",
    "# 重新变换回原始范围的预测值\n",
    "predictions = np.array(predictions)\n",
    "actuals = np.array(actuals)\n",
    "\n",
    "# 获取日期以便绘图\n",
    "dates = df.iloc[seq_len : len(predictions) + seq_len][\"Date\"].values\n",
    "\n",
    "# 绘制结果\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# 绘制训练和预测曲线\n",
    "plt.plot(dates, actuals, label=\"实际收盘价\", color=\"blue\", alpha=0.8)\n",
    "plt.plot(dates, predictions, label=\"模型预测\", color=\"red\", alpha=0.8)\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title(f\"股票 {stock} 收盘价预测对比\", fontsize=16)\n",
    "plt.xlabel(\"日期\")\n",
    "plt.ylabel(\"股价\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "\n",
    "# 添加网格\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "\n",
    "# 计算指标\n",
    "rmse = np.sqrt(np.mean((predictions - actuals) ** 2))\n",
    "mae = np.mean(np.abs(predictions - actuals))\n",
    "mape = np.mean(np.abs((actuals - predictions) / actuals)) * 100\n",
    "\n",
    "# 显示评估指标\n",
    "plt.figtext(\n",
    "    0.15,\n",
    "    0.85,\n",
    "    f\"RMSE: {rmse:.2f}\\nMAE: {mae:.2f}\\nMAPE: {mape:.2f}%\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.8),\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(OUTPUT_DIR, f\"stock_{stock}_prediction.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "b9e3edca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练LightGBM模型...\n",
      "保存LightGBM模型到 ./../../model\\lightgbm_model_600519.txt\n",
      "\n",
      "模型性能对比:\n",
      "指标         IndRNN-LSTM     LightGBM       \n",
      "----------------------------------------\n",
      "RMSE       1128.47         14.08          \n",
      "MAE        913.04          4.84           \n",
      "MAPE       117.09         % 0.85           %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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niYmJ4fnnn2fgwIFGI2jJycnY29ub7KNRo0ZG/zgkJyej0WhM3vHdqFEjbt++TWpqKs888wzHjx/HYDBw4cIFWrRowblz57CxseH1118nKCgo3+/RwIED1etrHyXv7t+bN28a3UUOfxV9D448irInhZ0QpSQ0NJSJEyfi7u6Op6cn27dvp2/fvnTu3Jnhw4fne/M7deoUAQEBuLm5MWzYMH766SfWr1/P3bt31f/ER40axZ07dwD473//C2A0X1qLFi3Ur/Mu9n/whoM8NWvWNNmemJjIlClTgNyRjMjISH7//XdGjRqV7263atWq8dxzz6kjdcePH883GmJubs7Jkye5ffu2yRxlZ2fj6OionsJ52MWLFxk/fjxt27bl/fffN7nO4sWLWbduHZB7qjGPRqPh1q1b+Ubb4uPj8426tGvXTj1l/DAbGxv+8Y9/GI1CXrx4EVdXV95//32ju2LPnDmDm5vbY6cRuXfvHr/++itubm5G7XPmzGHOnDlGbQ+fOjR1A8qhQ4do2rRpvpFAGxsbYmNjqVKlijqNSmlISUl57A0pgJqz4sgr7AqyYMEC/vOf/2BmZlbg3b7Lli3LV7w4ODjg4+ODpaUlWq32kTdpPChvmpOsrKx8o3E3btxQC+mLFy8ybdo0ddknn3zCN998w+uvvw7k3slb0Ij1s88+S0pKCunp6dja2qLT6ahatarJEem8YvHPP//kmWeeUU/v//rrr3Tv3p2uXbvyxx9/sH37dr7//nt27txJ586d1e0LO12Lm5sbO3fu5LPPPmP9+vVqe2JiIkuWLAGQO2PLmRR2QpSSOXPm8OKLL7Jv3z7MzMwYO3YsLi4uKIpi8nqb+fPnM2rUKBYsWIBGoyE9PZ3nn3+eyMhIdR64B6+V+/rrrwEKnBQ17xTTg/OZzZs3T51EF6BTp07qCBfkno75z3/+Y9RPixYtCvxPvmXLlhw5cgSDwUBCQgIBAQHqSFMeJycnnJycjNoyMzOZPn06y5cv58cff8TFxQVbW9sC75qFv564YGlpqRYwkZGRRkXhg3+UC5rPzcHBQZ1GIicnB4PBUOAdgnn9/PzzzyZPwa1YsSLfnYhguvh6kMFgyNd269Yt2rVrR5cuXZg/f36RTicvWbJEPWX+sKCgIPz9/bG0tCQrK8tkbIqicP/+fe7du5dvxKswUlJS8v3cmOLh4VHswi7vGrv79++rcws+eOdm27ZtqVOnDhkZGfzrX/8y2nb+/PlkZGTg6elpsu8XXniB//3vf8WK67nnnlOvccwTEhJCamoqWq2WTz/9FG9vbzIyMli1ahVhYWH07duXhIQEHBwcMDc3L/B7nfezm5GRga2tbaHXzfP666/z+eefG/3D9/PPP+Pp6cno0aPVUf2iGDlyJMuXL2fDhg3cunULHx8frl69ytKlS0lNTcXd3Z3mzZsXuV9RcqSwE6IU/O9//yMlJYVu3bqpxYazszO1atVSbzZ4WIMGDQgJCVELCFtbWzp06MDGjRu5fv16kU9v5O33wZsSwsLCjP6A5V2bk8fDw4Po6Ggg94/17t27CQgIwN3dnXXr1vH2228b7aNly5Zs2rSJffv2kZmZScuWLdm1a5fJeK5evco333zD/v37uXPnDnPnzqVv377Y2dlRs2bNR9488a9//Uv9Y71lyxb69OnDrl276NevH7Vr16Z27dqcPHnS5PE/PAJTq1YtJk2aVOC+HpaTk8NLL73E+PHj1ba8U8Svv/660TVY586dY/bs2SZHl+Li4jh48KD6NIyH/fe//+WPP/7gpZde4rvvviswnuzsbPUGjQEDBuDi4kKHDh0eeQzPPvusydP4kP+asXv37hV54uK8f1hKU2pqKkCB/2T06tWL/fv3ExoayocffqjOd3j58mVmzJhBnz59cHBwMLntf//7X3Jycor0aLG8fwpMfS/z5nIMCQkxGlH/9NNP0ev1/Pe//+Wbb75h9OjRVK1atcBT/XmFa97nqlWr5vvHqaB1H/ydeVDnzp3p2bMn27Zt4/Tp0/lGjR/nueeeIyoqimHDhhEVFUVUVJTR8unTpxepP1HypLATohTUrFkTOzs74uLiyMnJwczMjD///JObN2/mO12Z56233so3upT333lx/mjm3bGbkpKituVNtmpqQuKH2dnZ8dZbb1GjRg06d+7Mxx9/nK+wy7vWJ+8apVatWuUr7BYsWMCGDRs4cuQIiqJgaWnJs88+y7lz59TTwcHBwWi12nx/JO/cucPs2bPp1asX7u7u6PV69TqyAwcOkJOTw8aNG0v1j8mj5vMqSFZWVr62ffv2MWrUqMdu+/333xtdBP8or732Wr7rnAoycuRIMjMz1bkCv/jiC+7evUtQUBCKonDv3j3u3bv32NHG8pJX2AUFBak3t/zyyy9s3LhRXef999/ns88+46uvvlIvKVi2bBlAvtxnZ2eTlJSEra3tI68BLYxr166pz++1tbXlzz//xMzMzOT1t926deO///2vei2lk5MTiYmJJvvN+ycs7zSrk5MTv/32G3fu3Ml3p+vD6z6Km5sb27ZtU284Kap27dpx6tQpEhISuHLlCt9++y0rVqygW7dudO3atcj9iZJVMX+DhXjCWVhYMG/ePIYPH46Hhwdt2rRRpwCYMGGCyW1KepqJvALo4dNEQJGew9q2bVsg92L+h0cL8orUr7/+mipVqph8KH3NmjWJjY1l2LBh+Pv7ExoaSnR0tNE1ftOmTUNRFJKSkoxGJi9evMjs2bPp2LFjvln4p02bxuuvv17gs0FLyr1790hISMh3dyQUfPNE3mnwB73//vsMHDgQrVZrVMCvWLECf39/evfuzYYNG4xGjGbNmsXMmTNZtWoVAwcOBHJHivR6PZmZmQXeaWnKgyOO8OTdFZt3zejo0aPVkbf79+8bFXaNGzfmrbfeYt68ebzzzjtUr16dBQsW4OHhYTQyDbn/NDw4PUxJ+P7773nzzTepXr065ubmJr8/eafh8y4naNWqFSdOnODs2bM0btzYaN1Dhw5RvXp19R+8Vq1asX37do4cOUK3bt3yrQuoTynJe7axqaeW5E2wnXdTTnFoNBqaNGlCw4YN8fX1xdzcvFDXWYrSJ4WdEKXg9u3brFu3jo4dO3L58mWWLVtG06ZNWb58eYEjZUWdAsPMzOyRF5TnjUIcPHiQtLQ0o7s+TRV7BXmwmHv4+p569ephb2/P9evXadasmcnTWAMHDqRz5875nvH6MA8PD+Lj4/nzzz/V58o+ipmZmdHF36XF1IhdQTdPPEqVKlXyHdeqVasYMWIEDg4OTJs2TR1Ng9xHNv3nP//Bx8eHwYMHG21XEZ8MUdri4+OxtbUt8HRqnv/85z9s27aNd955B0dHRzIyMggJCcm33jPPPMPu3buxtrZ+5OnXESNGcOLECfUOXFOysrK4d++eei1bq1at2LNnj3rTw4Pi4uKAv2506tu3L6tWrWLRokV8/vnn6nqHDh3i999/N3r0X9++fZkxYwaLFi0yKuwuXbpEdHQ0rVu3pkaNGmRkZGBvb4+Xl5d6WjiPXq9X5zUsiWvhlixZwrVr1/j4448LfHyZKFtS2AlRCiIiIti7dy/Hjh0zmki0JLm4uPB///d/Rn88cnJy0Gg0aDQann/+eTw9Pdm1axdBQUHqH420tDSTj9sqyGeffQbk/iEyNSVI3nV1pqZggNy77R5X1AG88847jBo1itWrVzN8+PBCx1dacnJyOHToEFWqVMHKysroFGXe6FFqamq+521mZmZy//59rKysCjztDrnF/w8//ECdOnVITk6mVatW1K5dm06dOtGuXTumTZtGo0aN/vZUHJVBamoqf/zxB+7u7o9d9/nnn2fmzJlMnDiRuLg4Jk+erI46P8jS0jLfPwaTJk2iVatWRnO/5Y26PTh9zY8//oiVlVWBpx3fe+89du/eTVBQkHozFOTeNR0WFka1atXo3bs3kHsHaZMmTVi0aBFNmzbFz8+P33//XX3u8YM3m7Ro0QIvLy++//57pk+fzr///W9u3LjBwIEDyczMVNe1sbHBw8ODLVu2sHPnTry8vIDcAnTEiBH8+eeftG/fPt8IYVHlXSpRq1YtZs6c+bf6EiVHCjsh4JEzrYeFhRVqBOlBrVq1wtzcnF69euHl5UWtWrWwsbGhRo0atGzZskROHw4ZMoQ9e/bwxhtv0LFjR3Q6HTt37iQ2NhY7OzsAwsPD+cc//sEXX3yhPhB8586d6uTAD3twupObN2+yf/9+4uPjsbKyUicDflheYfeoIqYwBg8ezMSJE4mOji5yYZd3DaKiKCbvXi2OjIwMPDw8qFKlCubm5pibm6t9542Ufv/99+zZs8dou8zMTDIzM2nevLn6vFdTatSooZ5GvHLlCocPH+bAgQMsXryYb775Bsgtij///HO8vLx4+eWXC3VseU+pqEy2b99OdnY27du3N2o3NWKdkZGhTuQLuaecPTw88p26fNCdO3cYNWoUa9euxd7enpdffpnnn3/e5LrXr19n6NCh3Lhxg9dff51PPvkk380r//znP9m0aROff/45Bw8epH379vzvf/9j69at3L9/nxUrVqh3H5ubm7Np0yY8PT0ZPnw4gYGB3L9/H0VRGD16dL7n+0ZERNC5c2dmzJjB3LlzycrKIjs7m969e+Pv76+uFxISwv79++nRowevvfYazz77LL/++iv/+9//cHZ2ZtWqVQXmo7BmzJjBrVu3WLZsWaGu7RNlpOyeXiZExUMhnp15586dfNs97vmuBoNB6d+/v1K1alWlfv36SpUqVYz6fPPNN5Xs7OzH9uXr62v03NWHhYeHK02aNFG0Wq1SrVo15Y033lAyMjKM1klOTlYGDx6sPPvss4qNjY3SrVs3ZceOHQqgTJs2TVEU4+e/5n3Y2toqbm5uykcffaScPXtW7S9v3bxt169frwDKoUOHChXzW2+9pdjb25tc9ueffxq9/u233xRACQsLM7l+nvbt2yuAkpycrPj5+SkBAQFKly5dFEDp2bOnMnbsWGXs2LEKoNSsWVN9PWbMGCUgIEAZPny4snjx4kfu40GJiYkKoLz//vuF3qYgOTk5SlxcnDJ9+nSlfv36ikajUd577z0lJCREee211xQzMzMFUOzt7ZUhQ4Yo33//vXL//v0C++vdu7cCKFlZWYqiKMr169eVc+fOKUlJScrVq1fVj9atWytOTk7q6+TkZCUxMVE5c+aMYjAY/vZxlaTXX39dAZQjR44oiqIoFy5cUM6ePav07NlTAZQrV64oiqIo+/btU1566SUFUEaNGqUsXbpUsbKyUp+DvHHjRiUtLU3tNyUlRZk3b55Su3ZtBVDeeecd5caNG0b7/sc//qGYm5sbtf3666/Km2++qf6ueHt7KydOnDBaJysrS/nyyy+V1157TXn22WeVatWqKW3btlW2bt1q8hhTU1OV4OBgxcPDQ3nzzTcLXE9RFEWv1yuhoaFKp06dlG7duilfffWVkpOTk2+9s2fPKm+//bZSs2ZNpUqVKkqzZs2U4ODgxz47dtq0aQU+KzbPmTNnFAsLC+WVV14xuW9RfjSKUsn+tROiAvD19SU6OpoTJ06od6cqioJOp2PChAmEh4cTGxtrNL/U0+LNN9/k0KFD3Lp1q8B15s+fz8qVK7ly5Qp37txh69at6qkrU1555RWOHj1KQkICLVq0UEfZHjfZrKIo6kSzffv2VSc63rBhAzdv3sTKygpLS8t8I2UFTXfyoLx+27Vrx8svvwzAtm3b2LdvH/fu3ePOnTtcvHiRM2fOcOfOHapUqcI777xDQECA0Wnta9eusW3bNlavXs3BgweB3MmhV69eTZ8+ffLtt3v37uzYsYOMjAyqVKnClClTCjXH3IOuXr1q8qL78rB582beeustmjZtqk5p895777F27Vog9zrPL774gs8//5y9e/dibW3NwoUL+fDDD4HcZxj7+flx7NgxIPdmoLwnu+zevZt+/fpha2vL559/zltvvaXud/LkyVy+fJmNGzdSs2ZNkpOT88W2f/9+Ro0axe+//05MTIz6fRaiPElhJ0Qp6NSpE3v27KFjx440a9aMqlWrkpmZSVJSEj/++CO2trb8/vvvatH3NOnSpQsHDhwocEJdyL3A/OWXX8bNzY3+/fsTFBT0yNOQLVq04MSJE9y4cUN9/Nrf4e7uzrFjx9SnERT19O6DBeMnn3yiTiydmppK/fr10el0PPPMM7z44ou0adOGTp064eXl9dhJiRMSEli2bBmZmZksWrTI5DqdO3fml19+4c6dO9jZ2REbG0tCQgJardbo5oyH5d1tq9fr6devX5EvPygtiqKwbds2FEWhb9++AGzcuJGlS5fSoUMHBg8ezNWrV/Hw8OC1115j2bJl+a4dUxSFLVu2MG3aNCIiIowKsF9//ZXGjRvnu4N19uzZ/Pvf/6Z27dp8/vnn9O/f32R8WVlZHDt2jHbt2pXwkQtRPFLYCVEKbty4wX/+8x92797NpUuXyMjIoFq1ajRo0IBu3brx8ccfU79+/fIOs8JS/v8zXCvj3Z/nz5/Hzs6uRApQ8ZfY2NjHXuepFOEazOvXr3PlyhWaNWtWYef2E8IUKeyEEEIIISqJwj3tWAghhBBCVHhS2AkhhBBCVBJy4cATIicnh+TkZKpVq1Zi83QJIYQQouJTFIW7d+9Sr169x97tL4XdEyI5OVl98LUQQgghnj6XL1/GycnpketIYfeEqFatGpD7ZIC8GctF7gO1d+3ahaenJ5aWluUdToUiuTFN8mKa5MU0yYtpkpeClUZudDodzs7Oai3wKFLYPSHyTr9Wq1Yt33xLTzODwYCNjQ3Vq1eXN5eHSG5Mk7yYJnkxTfJimuSlYKWZm8JciiU3TwghhBBCVBJS2AkhhBBCVBJS2AkhhBBCVBJS2AkhhBBCVBJS2AkhhBBCVBJS2AkhhBBCVBJS2AkhhBBCVBJS2AkhhBBCVBJS2AkhhBBCVBJS2AkhhBBCVBJS2AkhhBBCVBJS2JWx//3vfxw5coT09PTyDkUIIYR46k2cOJGePXsWuj2Pl5cXERERpRhZ8ZRLYXfr1i369OmDra0tbdu25cSJE4XaLjo6GhcXl9INrpDOnz9PjRo18rX36tULjUajfnTp0kVdtmDBAho3bsyQIUNwcnJi3759ZRmyEEIIIR5w4sQJFi9ezMKFCwvVnmft2rX89NNPZRFikZVLYefr60t2djZxcXH069cPHx8fsrKyyiOUYrlw4QLdu3fnzp07+ZYdPXqUkydPcufOHe7cucO2bduA3EJw7ty5xMfHk5CQQEBAAMHBwWUduhBCCCGAnJwcPvzwQ/71r3/RsGHDx7bnuX37NmPHjqVx48ZlGW6hWZT1Ds+fP8+OHTtITk7GwcGBcePGMW/ePA4fPkz79u3LOpxi6dmzJx9++CHjx483ar9y5QqKotC0adN82+j1esLDw3F0dASgdevWbN68ucj7bjfnZ7IsbIsXeCWkNVcIaQtNp/+EPltT3uFUKJIb0yQvpkleTJO8mPak5uXi3B7q119++SUnT57kww8/ZPv27Xh5eWFlZVVge56xY8fSt29f7t27Vx6H8FhlXtgdOnSIhg0b4uDgAIC5uTkBAQFYW1sTExPD6NGjuXTpEt7e3ixevBg7O7tH9hcREUFERATR0dEAXLx4EVdXVxRFwcXFBS8vL9avX4+fnx8JCQkcOXKEnTt3Eh8fT0REBIMGDWLKlCkALFmyBB8fn8ceww8//IBGo8lX2B05coTs7GycnJy4c+cOPXv2ZMmSJTz77LM0adKEJk2aAJCens6iRYvo27dvgfvQ6/Xo9Xr1tU6nA0BrpmBurjw2xqeF1kwx+iz+IrkxTfJimuTFNMmLaU9qXgwGAwBpaWlMmzYNV1dXLly4wOrVq5k1axY//fSTyfaff/6ZKlWqEB0dzc8//0xsbCyBgYFkZ2erfT68j4fbSyLuwijzwi45ORl7e3ujtmnTpnH58mXc3NwICwujc+fOBAYGMmTIELZu3fq39qfT6QgNDWX48OFs3bqVzMxMdu3ahaOjI6dOneK7777jwIEDLF++nMDAwEIVdq6urly8eDFf+5kzZ2jRogWffvopZmZm+Pn5MXnyZL788kt1ncjISPr370+DBg0eeSp2zpw5zJgxI1/7lFY52NhkF+7gnyKz2uSUdwgVluTGNMmLaZIX0yQvpj1peYmMjARgz5496HQ65s+fT/Xq1WndujUBAQF8/PHHJtsnTZpEx44dCQwMZNiwYezbt48///yTuLg4tc+HRUVFlVjcGRkZhV63zAs7g8GAubl5vvavv/4ad3d3PvjgAyB39MzJyYlr165Rp06dYu9v8ODBWFtb4+DgQO/evdmyZYta+aanp7Nq1Srs7e0ZNmwY8+bNK/Z+ACZPnszkyZPV16Ghofj4+BgVdp6ennz//fd8/PHHTJ48mU8//bTAvsaMGaO+1ul0ODs788lxM7Is8+fvaaU1U5jVJofgo2boc56c0wFlQXJjmuTFNMmLaZIX057UvJya3g3IvTnC3d2d/v37q8vWr1/Ppk2baN++fb72KlWqcOzYMV5//XWmTp0KwObNm2nRogXdu3c32ofBYCAqKoquXbtiaWlZInHnnbUrjDIv7Ozs7EhJSTFqa968OWlpaXh6eqptjo6OaLVakpKSilTYPVzVWltbG31+kJubmzp6+OD585Jib2/PrVu30Ov1aLVaACwsLPDw8ODzzz/Hx8enwMJOq9Wq2zwoZmIXatasWeKxPqkMBgORkZEcm+pVYr9AlYXkxjTJi2mSF9MkL6Y96Xlp0KAB9+/fN4r98uXL/Pvf/2bnzp352v/xj3+wYMECbty4Qe3atYHcemPTpk0cO3aMxYsX59uHpaVlieWmKP2U+V2xLVu25OzZs9y9exeArKwsEhMTGTZsGBcuXFDXS05ORq/X06BBg0f2p9FoyMn5ayj42LFjhY6levXqRYz+0d59913279+vvj506BAODg5otVq++eYbPvvsM3WZlZWVyZFLIYQQQpSuHj16kJCQwJdffsmff/7J559/TlxcHEOGDDHZ7uPjw759+zh16hSxsbHExsbSq1cvZs6cycyZM8v7cIyU+Yidu7s7bm5u+Pv7M3PmTMLDw7Gzs8PX15e5c+eybNkyunTpQmBgIH369FFvsiiIo6MjCQkJ6HQ69Ho9ISEhZXQk+TVr1ox//etfhIWFcfPmTSZPnoy/vz8AjRs3xs/Pj4YNG9KqVStmzJjB22+/XW6xCiGEEE+rmjVrEhkZybhx4xgzZgx169bl22+/pUGDBibbnZ2d8/VRtWpVatWqRa1atcrhCApW5oWdmZkZ27dvx8/Pj6ZNm9K8eXMiIyNxdnbmxx9/JCAggPHjx+Pt7c2SJUse21+nTp3w9PSkWbNmODg4EBQUxMCBA8vgSPKbOHEiiYmJeHl5Ua1aNUaMGEFQUBCQO1K5dOlSxowZQ0pKCm+99ZbRCJ4QQgghyk779u05dOhQodsfVhGfOgHlUNgB1K9fn127duVr9/DwIDY2tsDt3njjjXx3o5qZmbFu3TqjtgEDBgAYrZv39YPfiCFDhqhfu7i4oCiFv23b1PqWlpasWLGCFStWmNxm4MCB5VZ0CiGEEKLyk2fFPiQpKQk7OzuTHw/eJSOEEEIIUdGUy4hdRVavXr0CRw1tbGzKNhghhBBCiCKQwu4hFhYWuLi4lHcYQgghhBBFJqdihRBCCCEqCSnshBBCCCEqCSnshBBCCCEqCSnshBBCiELy8vIiIiICRVEICQmhUaNG1KpVi5EjR5Keng7A9OnT0Wg0+T6io6PLN3jxVCiXwu7WrVv06dMHW1tb2rZty4kTJwq1XXR0dIW4sSE8PJy6detiaWmJh4cHV69eVZft3bsXNzc3atWqxfz5842227RpEw0aNKBevXqsX7++rMMWQgjxN6xdu5affvoJgBUrVrBw4ULWrl3LgQMHOHLkCB999BEAkyZN4s6dO+pHbGwstWvXplWrVuUZvnhKlEth5+vrS3Z2NnFxcfTr1w8fHx+ysrLKI5Qi279/P8HBwaxZs4bExEQURWHcuHEA3Lhxg169ejFgwAAOHTrE2rVr2bNnDwCnTp1i0KBBBAcH89NPPzF16lTOnj1bnocihBCikG7fvs3YsWNp3LgxAKtXr2bMmDG0bduWxo0bM2PGDLZt2waAtbW10RyoixYtIjAwkGeeeaY8D0E8Jcp8upPz58+zY8cOkpOTcXBwYNy4ccybN4/Dhw/Tvn37sg6nyM6dO8fSpUvp0qULAEOHDiU0NBTI/W+uXr16BAcHo9FomDp1KitWrKBjx44sX76cjh074ufnB8DHH3/MmjVr+OSTT4q0/3ZzfibLwrZkD+oJpjVXCGkLTaf/hD5bU97hVCiSG9MkL6ZJXvK7OLeH+vWECRPo27cv9+7dA+DmzZvUr19fXW5ubo65uXm+PpKTk9myZQuJiYmlH7AQlENhd+jQIRo2bIiDgwOQ+8sQEBCAtbU1MTExjB49mkuXLuHt7c3ixYuxs7N7ZH8RERFERESo1y5cvHgRV1dXFEXBxcUFLy8v1q9fj5+fHwkJCRw5coSdO3cSHx9PREQEgwYNYsqUKQAsWbIEHx+fR+5v6NChRq/Pnj1Lo0aNAIiLi6Njx45oNLlvim3btmXSpEnqMm9vb3W7tm3bMnPmzAL3o9fr0ev16mudTgeA1kzB3Lzwjz6r7LRmitFn8RfJjWmSF9MkL/kZDAYMBgMnT57kl19+IS4ujsDAQLKzs2nZsiVbtmyhT58+AHz11Vd07twZg8Fg1MeiRYt499130Wq1+ZY9yfKOpTIdU0kpjdwUpa8yL+ySk5Oxt7c3aps2bRqXL1/Gzc2NsLAwOnfuTGBgIEOGDGHr1q1/a386nY7Q0FCGDx/O1q1byczMZNeuXTg6OnLq1Cm+++47Dhw4wPLlywkMDHxsYfeg27dvs3TpUvVZtTqdjpdeekldXr16dZKTk9Vlrq6uJpeZMmfOHGbMmJGvfUqrHGxssgsd49NiVpuc8g6hwpLcmCZ5MU3y8pfIyEgyMzNZsmQJw4YNY9++ffz555/ExcXRqVMnZs6cSfPmzbl37x6XLl3iP//5D5GRker22dnZLFmyhJkzZxq1VyZRUVHlHUKFVZK5ycjIKPS6ZV7YGQwGk8PVX3/9Ne7u7nzwwQdA7uiZk5MT165do06dOsXe3+DBg7G2tsbBwYHevXuzZcsWtfJNT09n1apV2NvbM2zYMObNm1ekvkeOHIm7u7s6EmdhYYFWq1WXW1tbq9+MRy0zZfLkyYwZM0Z9rdPpcHZ25pPjZmRZ5s/f00prpjCrTQ7BR83Q58jpowdJbkyTvJgmecnv1PRu/Pvf/+b5559n8uTJWFpasnnzZlq0aMHgwYMZPHgwZ86cYfLkyTRq1Ijx48cbbf/zzz9Tt25dhg8fXk5HUHoMBgNRUVF07doVS0vL8g6nQimN3OSdtSuMMi/s7OzsSElJMWpr3rw5aWlpeHp6qm2Ojo5otVqSkpKKVNg9XCxZW1sbfX6Qm5ubOnpoZWVV6H0ArFq1ij179hAXF6e21ahRgxs3bqiv7969q/b7qGWmaLVao0IwT8zELtSsWbNIsVZmBoOByMhIjk31kjeXh0huTJO8mCZ5MW3jxo1cu3aNevXqAbl/YzZt2sSxY8dYvHgxNWvW5JdffuHgwYP58vbdd9/Rr1+/Sp1PS0vLSn18f0dJ5qYo/ZT5XbEtW7bk7Nmz3L17F4CsrCwSExMZNmwYFy5cUNdLTk5Gr9fToEGDR/an0WjIyfnr1MGxY8cKHUv16tWLGH2uo0ePMmrUKDZs2KBeKwjwyiuvcOjQIfX18ePHcXR0fOwyIYQQFdMvv/zCwoUL+fXXX4mNjaVXr17MnDlTvUb6k08+4e233zY5lcnOnTt54403yjhi8bQr88LO3d0dNzc3/P39uXDhAlOmTMHOzg5fX18OHjzIsmXLSExMxN/fnz59+hgVTqY4OjqSkJCATqfjxo0bhISElGr8169fp2fPnkyYMIE2bdqQlpZGWloaAL169eLAgQPs3r0bg8FASEgI3bp1A6Bfv35s2LCBkydPkpaWxueff64uE0IIUTE5OTnh4OCAi4sLLi4uVK1alVq1alGrVi3Onz/PunXrmD17dr7t/vjjD5KTk2nbtm05RC2eZmVe2JmZmbF9+3auX79O06ZNiY6OJjIyEmdnZ3788UcWLVpEq1atsLGxYeXKlY/tr1OnTnh6etKsWTN69OhBUFBQqca/fv16rl27RnBwMNWqVVM/AGrVqkVYWBjdu3fHwcGBs2fPqnfctmjRgoCAANq0aYOjoyPm5uaMGDGiVGMVQghRsiIiIhgyZAgAzz//PKmpqTg5OeVb77nnniMrK4uqVauWcYTiaadRFEXubS9hiYmJnDlzhg4dOuT7pU5ISODKlSt4eHgU6bo+nU7HM888w82bN+UauwfkXRfUvXt3uc7jIZIb0yQvpkleTJO8mCZ5KVhp5CavBkhNTX3sZWRlfvNERZeUlETz5s1NLvPy8mLDhg2P7cPV1dVoapMHvfTSS0ZTogghhBBClBQp7B5Sr149YmNjTS6zsbEp22CEEEIIIYpACruHWFhY4OLiUt5hCCGEEEIUWZnfPCGEEEIIIUqHFHZCCCGEEJWEFHZCCCGEEJWEFHZCCCEqFS8vLyIiIoza3n33XUaNGmXU1qtXLzQajfrRpUuXMoxSiNIhN0+Usf/9739cunSJJk2aYGtrW97hCCFEpbJ27Vp++ukn+vfvr7ZFRkYSHR3N2bNnjdY9evQoJ0+eVCcYlvnYRGVQLiN2t27dok+fPtja2tK2bVtOnDhRqO2io6MrxB2r4eHh1K1bF0tLSzw8PLh69arR8vPnz1OjRo182y1YsIDGjRszZMgQnJyc2LdvX1mFLIQQld7t27cZO3YsjRs3VtvS09MZMWIEc+bMwc7OTm2/cuUKiqLQtGlT7OzssLOzk3+2RaVQLoWdr68v2dnZxMXF0a9fP3x8fMjKyiqPUIps//79BAcHs2bNGhITE1EUhXHjxqnLL1y4QPfu3blz547RdufPn2fu3LnEx8eTkJBAQEAAwcHBZR2+EEJUWmPHjqVv3768+uqratuMGTPIzMzEwsKCqKgocnJyADhy5AjZ2dk4OTlha2tL//79871vC/EkKvNTsefPn2fHjh0kJyfj4ODAuHHjmDdvHocPH6Z9+/ZlHU6RnTt3jqVLl6rXYgwdOpTQ0FB1ec+ePfnwww8ZP3680XZ6vZ7w8HAcHR0BaN26NZs3by5wP3q9Hr1er77W6XQAvD5vN1mW8l9lHq2Zwqw28PLMnehzNOUdToUiuTFN8mLak5yXU9O7ER0dzc8//0xsbCyBgYFkZ2dz/vx5Fi5cyMsvv8y5c+cICwvD0dGRzZs3Ex8fT/PmzZk7dy5mZmZ89NFHTJw4kUWLFhn1bTAYjD6LXJKXgpVGborSV5k/K3bNmjXMnDmTc+fOqW0zZszgzTffJD09ndGjR3Pp0iW8vb1ZvHix0dB5dHQ0Q4YM4eLFi2pbREQEERERREdHA3Dx4kVcXV1RFAUXFxe8vLxYv349fn5+JCQkcOTIEXbu3El8fDwREREMGjSIKVOmALBkyRJ8fHyKdDyTJk3i9OnTbNu2Dch9TqxGo1FjMCU9PR0fHx/atWvHzJkzTa4zffp0ZsyYka993bp18gQMIYR4QGZmJoGBgQwbNow2bdqwcOFCmjZtys2bN4mKimLx4sVYWVlx7949PvjgA8aOHUurVq2M+oiPj2fu3LmsWbOmnI5CiIJlZGQwcODAivms2OTkZOzt7Y3apk2bxuXLl3FzcyMsLIzOnTsTGBjIkCFD2Lp169/an06nIzQ0lOHDh7N161YyMzPZtWsXjo6OnDp1iu+++44DBw6wfPlyAgMDi1TY3b59m6VLl7Ju3Tq1zdXV1ajwfFhkZCT9+/enQYMGjzwVO3nyZMaMGWN0HM7Oznxy3IwsS/NCx1jZ5Y4y5BB81OyJG2UobZIb0yQvpj3JeembfYzXX3+dqVOnArB582ZatGjBoUOH6NGjB3369FHXnT9/PjVr1qR79+5GfTRs2JB///vfdO7cGa1Wq7YbDAaioqLo2rWr3FzxAMlLwUojN3ln7QqjzAs7g8GAuXn+wuTrr7/G3d2dDz74AMgdPXNycuLatWvUqVOn2PsbPHgw1tbWODg40Lt3b7Zs2aIOaaanp7Nq1Srs7e0ZNmwY8+bNK1LfI0eOxN3dHW9v70Jv4+npyffff8/HH3/M5MmT+fTTT02up9Vqjd5c8sRM7ELNmjWLFGdlZjAYiIyM5NhUL3lzeYjkxjTJi2lPcl5cXUdx48YNateuDeSObmzatIn79+/z7rvvqseTk5PDlStXqF+/Pu+99x6jRo3iH//4B5B7h6yDgwNVq1Y1uQ9LS8snLi9lQfJSsJLMTVH6KfPCzs7OjpSUFKO25s2bk5aWhqenp9rm6OiIVqslKSmpSIVdRkaG0Wtra2ujzw9yc3NTRw+trKwKvQ+AVatWsWfPHuLi4oq0nYWFBR4eHnz++ef4+PgUWNgJIYQonH379hndgDdu3DheffVV2rVrh5eXF5s3b6Zdu3Z88cUXGAwGunTpwsmTJ/nXv/5FWFgYN2/eZPLkyfj7+5fjUQhRMsq8sGvZsiVnz57l7t27VKtWjaysLBITE5k4cSIxMTHqesnJyej1eho0aPDI/jQajXqXE8CxY8cKHcvjzlMX5OjRo4waNYrt27fj4OBQqG2++eYb/vzzT8aOHQvkFpKmRi6FEEIUTd48dHmqVq1KrVq16NChA+vXryc4OJjff/+d559/nm3btmFra8vEiRNJTEzEy8uLatWqMWLECIKCgsrpCIQoOWVe2Lm7u+Pm5oa/vz8zZ84kPDwcOzs7fH19mTt3LsuWLaNLly4EBgbSp0+fxxZOjo6OJCQkoNPp0Ov1hISElGr8169fp2fPnkyYMIE2bdqQlpYGUODwfZ7GjRvj5+dHw4YNadWqFTNmzODtt98u1ViFEOJp9OBTJ3r16kWvXr3yrWNpacmKFStYsWJFGUYmROkr83nszMzM2L59O9evX6dp06ZER0cTGRmJs7MzP/74I4sWLaJVq1bY2NiwcuXKx/bXqVMnPD09adasGT169Cj1/7jWr1/PtWvXCA4Oplq1aurH47Rs2ZKlS5cyZswYWrVqRYMGDfjss89KNVYhhBBCPF3K5ZFi9evXZ9euXfnaPTw8iI2NLXC7N954I98dp2ZmZkZ3pQIMGDAAwGjdvK8f/E9uyJAh6tcuLi4FTk/yoICAAAICAh65TkF9DRw4kIEDBz52H0IIIYQQxVEuT56oyJKSktTHyzz88eCzB4UQQgghKppyGbGryOrVq1fgqKFMDCyEEEKIikwKu4dYWFjg4uJS3mEIIYQQQhSZnIoVQgghhKgkpLATQgghhKgkpLATQghRrv73v/9x5MgR0tPTyzsUIZ54UtgJIYQoU15eXurUUwsWLKBx48YMGTIEJycn9u3bp663d+9e3NzcqFWrFvPnzy+naIV4spRLYXfr1i369OmDra0tbdu25cSJE4XaLjo6usLc2HD+/Hlq1Khh1KYoCiEhITRq1IhatWoxcuRI9T/QRy0TQoinxdq1a/npp5+A3PfRuXPnEh8fT0JCAgEBAQQHBwNw48YNevXqxYABAzh06BBr165lz5495Rm6EE+EcinsfH19yc7OJi4ujn79+uHj42P0AOeK7sKFC3Tv3p07d+4Yta9YsYKFCxeydu1aDhw4wJEjR/joo48eu0wIIZ4Gt2/fZuzYsTRu3BgAvV5PeHg4jo6OALRu3Zpbt24BuQVgvXr1CA4OplGjRkydOlUe/yVEIZT5dCfnz59nx44dJCcn4+DgwLhx45g3bx6HDx+mffv2ZR1OsfTs2ZMPP/yQ8ePHG7WvXr2aMWPG0LZtWwBmzJihTmr8qGVF0W7Oz2RZ2P7NI6g8tOYKIW2h6fSf0GdryjucCkVyY5rkxbTSysvFuT3Ur8eOHUvfvn25d+8eAE2aNKFJkyYApKens2jRIvr27QtAXFwcHTt2RKPJjaVt27ZMmjSpxOISorIq88Lu0KFDNGzYEAcHBwDMzc0JCAjA2tqamJgYRo8ezaVLl/D29mbx4sXY2dk9sr+IiAgiIiKIjo4Gch8d5urqiqIouLi44OXlxfr16/Hz8yMhIYEjR46wc+dO4uPjiYiIYNCgQUyZMgWAJUuW4OPj89hj+OGHH9BoNPkKu5s3b1K/fn31tbm5Oebm5o9dZoper0ev16uvdTodAFozBXPzxz/67GmhNVOMPou/SG5Mk7yYVlp5MRgMQO6lND///DOxsbEEBgaSnZ2tLtuxYwfvvfce9evXZ9KkSRgMBlJSUmjcuLG6TpUqVUhOTlZfl5W8/ZX1fis6yUvBSiM3RemrzAu75ORk7O3tjdqmTZvG5cuXcXNzIywsjM6dOxMYGMiQIUPYunXr39qfTqcjNDSU4cOHs3XrVjIzM9m1axeOjo6cOnWK7777jgMHDrB8+XICAwMLVdi5urrme2Yt5J5G2LZtG2+//TaQW3R27dr1sctMmTNnDjNmzMjXPqVVDjY22YU59KfKrDY55R1ChSW5MU3yYlpJ5yUyMpLMzEwCAwMZNmwY+/bt488//yQuLo7IyEgAsrOzmThxIuHh4QwcOJChQ4dy48YN/vjjD6N10tPT1ddlLSoqqlz2W9FJXgpWkrnJyMgo9LplXtgZDAaTI1Vff/017u7ufPDBB0Du6JmTkxPXrl2jTp06xd7f4MGDsba2xsHBgd69e7Nlyxa18k1PT2fVqlXY29szbNgw5s2bV+z9AMyePRtvb2/at2/P3bt3OXnyJDExMY9dZsrkyZMZM2aM+lqn0+Hs7Mwnx83Isix4pO9pozVTmNUmh+CjZuhz5LTagyQ3pkleTCutvJya3o3g4GBef/11pk6dCsDmzZtp0aIF3bt3V9fr2bMnbdu25Z133mHjxo1ERkZSs2ZNdZ2UlBS0Wq3RNmXBYDAQFRVF165dsbS0LNN9V2SSl4KVRm7yztoVRpkXdnZ2dqSkpBi1NW/enLS0NDw9PdU2R0dHtFotSUlJRSrsHq5qra2tjT4/yM3NTR09tLKyKvQ+ClK/fn1OnTrFmTNnmDBhAnXq1KFDhw6PXWaKVqtFq9Xma4+Z2IWaNWv+7VgrC4PBQGRkJMemesmby0MkN6ZJXkwrzbx888033Lhxg9q1awO579ObNm3i008/xc/Pj7FjxwK5z+M2NzfH0tKSdu3asW7dOjWWU6dO4ejoWG7fM0tLS/l5MUHyUrCSzE1R+inzwq5ly5acPXuWu3fvUq1aNbKyskhMTGTixIlGI1jJycno9XoaNGjwyP40Gg05OX+dOjh27FihY6levXrRD+AxNBoN1atXZ/fu3Rw8eLDQy4QQorLat2+f0cwH48aN49VXX+WNN96gY8eONGzYkFatWjFjxgz1cpVevXoxcuRIdu/ejYeHByEhIXTr1q28DkGIJ0aZT3fi7u6Om5sb/v7+XLhwgSlTpmBnZ4evry8HDx5k2bJlJCYm4u/vT58+fdSbLAri6OhIQkICOp2OGzduEBISUkZHUrBPPvmEt99+m1atWhVpmRBCVEZOTk64uLioH1WrVqVWrVq0adOGpUuXMmbMGFq1akWDBg347LPPAKhVqxZhYWF0794dBwcHzp49q97oJoQoWJmP2JmZmbF9+3b8/Pxo2rQpzZs3JzIyEmdnZ3788UcCAgIYP3483t7eLFmy5LH9derUCU9PT5o1a4aDgwNBQUEMHDiwDI7EtPPnz7Nu3Tri4+OLtEwIIZ4WeU+dABg4cGCB79kfffQR3bp148yZM3To0IGqVauWUYRCPLnKvLCD3OvNdu3ala/dw8OD2NjYArd744038t2NamZmxrp164zaBgwYAGC0bt7XD76hDBkyRP3axcUFRSn8bf4Frf/888+TmppqcptHLRNCCJGfq6srrq6u5R2GEE8MeVbsQ5KSkrCzszP5UZwJhYUQQgghykq5jNhVZPXq1Stw1NDGxqZsgxFCCCGEKAIp7B5iYWGBi4tLeYchhBBCCFFkcipWCCGEEKKSkMJOCCGEEKKSkMJOCCGeAikpKRw+fJg7d+6U6jZCiPIlhZ0QQlRyGzduxMXFBT8/P5ycnNi4cSMAy5cvx9nZGRsbG7p06cK1a9ceu40QomKrcIXdrVu36NOnD7a2trRt25YTJ04Uarvo6OgKc9PD+fPnqVGjhlGboiiEhITQqFEjatWqxciRI0lPTy+nCIUQT4vU1FRGjBhBTEwMJ0+eZNGiRYwfP54//viDmTNnsm3bNs6cOUPDhg35/PPPH7mNEKLiq3CFna+vL9nZ2cTFxdGvXz98fHyMnjFY0V24cIHu3bvnO3WxYsUKFi5cyNq1azlw4ABHjhzho48+KqcohRBPC51Ox4IFC2jevDkArVu35tatWxw/fpxXX32V1q1bU79+fYYMGcLVq1cfuY0QouKrUNOdnD9/nh07dpCcnIyDgwPjxo1j3rx5HD58mPbt25d3eIXSs2dPPvzww3z/3a5evZoxY8bQtm1bAGbMmFGsCY/bzfmZLAvbEom1MtCaK4S0habTf0KfrSnvcCoUyY1pT1NeLs7tgbOzM4MGDQLAYDAQFhZG3759eemll/jll1+IjY3F1dWVL7/8kpYtWwIUuI0QouKrUIXdoUOHaNiwIQ4ODgCYm5sTEBCAtbU1MTExjB49mkuXLuHt7c3ixYuxs7N7ZH8RERFEREQQHR0N5D5WzNXVFUVRcHFxwcvLi/Xr1+Pn50dCQgJHjhxh586dxMfHExERwaBBg9SHTi9ZsgQfH5/HHsMPP/yARqPJV9jdvHmT+vXrq6/Nzc0xNzcvsB+9Xo9er1df63Q6ALRmCubmhX/0WWWnNVOMPou/SG5Me5ryYjAY1K/j4uLo1q0bVlZWnDhxAjs7O3x8fGjVqhWQ+5jEGTNmPHKbB5c9LfKO+Wk89keRvBSsNHJTlL4qVGGXnJyMvb29Udu0adO4fPkybm5uhIWF0blzZwIDAxkyZAhbt279W/vT6XSEhoYyfPhwtm7dSmZmJrt27cLR0ZFTp07x3XffceDAAZYvX05gYGChCjtXV9d8z7OF3FMZ27Zt4+233wZyi86uXbsW2M+cOXOYMWNGvvYprXKwscku/EE+JWa1ySnvECosyY1pT0NeIiMj1a8VRSEoKIivvvqK3r1707dvXzZv3kxISAiOjo5s2bKFWbNmUb16dTQajcltJk6cWF6HUu6ioqLKO4QKSfJSsJLMTUZGRqHXrVCFncFgMDmK9fXXX+Pu7s4HH3wA5I6eOTk5ce3aNerUqVPs/Q0ePBhra2scHBzo3bs3W7ZsUavi9PR0Vq1ahb29PcOGDWPevHnF3g/A7Nmz8fb2pn379ty9e5eTJ08SExNT4PqTJ09mzJgx6mudToezszOfHDcjy7Lgkb6njdZMYVabHIKPmqHPqdyn1YpKcmPa05SXU9O75Wvr2bMnL774Io0aNWLw4MEEBgYC0Lt3b+rUqYODgwNt2rQxuY27u/tjz5RUNgaDgaioKLp27YqlpWV5h1NhSF4KVhq5yTtrVxgVqrCzs7MjJSXFqK158+akpaXh6emptjk6OqLVaklKSipSYfdwxWttbW30+UFubm7q6KGVlVWh91GQ+vXrc+rUKc6cOcOECROoU6cOHTp0KHB9rVaLVqvN1x4zsQs1a9b82/FUFgaDgcjISI5N9ZI3l4dIbkx72vKyd+9efvjhB0JDQwGwtbVFo9FgZmbGzZs31RzodDr0ej1mZmYcPHjQ5DZarfapyJkplpaWT+2xP4rkpWAlmZui9FOhCruWLVty9uxZ7t69S7Vq1cjKyiIxMZGJEycajW4lJyej1+tp0KDBI/vTaDTk5Px1uuXYsWOFjqV69epFP4DH0Gg0VK9end27d3Pw4MES718IIR72wgsvEB4eTqNGjfD29mbKlCl4enrSo0cPfH19ad26NQ4ODixbtgw7OzuaNWtGamqqyW1K431RCFGyKtR0J+7u7ri5ueHv78+FCxeYMmUKdnZ2+Pr6cvDgQZYtW0ZiYiL+/v706dNHvcmiII6OjiQkJKDT6bhx4wYhISFldCQF++STT3j77bfVC5aFEKI01a1bl02bNrFw4UKaNGlCRkYGq1evpl+/fkyaNIkFCxYwZMgQUlJSmDx5MpaWlgVuI4So+CpUYWdmZsb27du5fv06TZs2JTo6msjISJydnfnxxx9ZtGgRrVq1wsbGhpUrVz62v06dOuHp6UmzZs3o0aMHQUFBZXAUBTt//jzr1q1j9uzZ5RqHEOLp0rVrV+Lj49HpdGzcuJHatWuj0WgIDg7m0qVLZGZmcuTIERo2bPjIbYQQFV+FOhULudei7dq1K1+7h4cHsbGxBW73xhtv5Lsb1czMjHXr1hm1DRgwAMBo3byvIyIi1LYhQ4aoX7u4uKAohZ8aoaD1n3/+eVJTUwvdjxBCCCFEUVSoEbuKLikpCTs7O5MfxZlsWAghhBCiJFW4EbuKrF69egWOGtrY2JRtMEIIIYQQD5HCrggsLCxwcXEp7zCEEEIIIUySU7FCCCGEEJWEFHZCCCGEEJWEFHZCCFEJpKSkcPjwYe7cuVPeoQghypEUdkII8YTbuHEjLi4u+Pn54eTkxMaNG4mIiECj0eT7eHBap/Pnz1OjRo3yC1wIUeLKpbC7desWffr0wdbWlrZt23LixIlCbRcdHV0hbl4IDw+nbt26WFpa4uHhwdWrV9Vle/fuxc3NjVq1ajF//nyT26ekpFC3bt188+4JIURRpaamMmLECGJiYjh58iSLFi1i/PjxDBw4kDt37qgfly9fplatWuozqi9cuED37t1lhE+ISqZcCjtfX1+ys7OJi4ujX79++Pj4kJWVVR6hFNn+/fsJDg5mzZo1JCYmoigK48aNA+DGjRv06tWLAQMGcOjQIdauXcuePXvy9TF+/HiuXbtW1qELISohnU7HggULaN68OQCtW7fm1q1bWFlZGc21uXr1avr27ctzzz0HQM+ePfnwww/LM3QhRCko8+lOzp8/z44dO0hOTsbBwYFx48Yxb948Dh8+TPv27cs6nCI7d+4cS5cupUuXLgAMHTqU0NBQANauXUu9evUIDg5Go9EwdepUVqxYQceOHdXtY2Ji2L59OzVr1izW/tvN+ZksC9u/fyCVhNZcIaQtNJ3+E/psTXmHU6FIbkyrTHm5OLcHzs7ODBo0CACDwUBYWBh9+/Y1Wu/+/fssXLiQw4cPq20//PADGo2G8ePHl2nMQojSVeaF3aFDh2jYsCEODg4AmJubExAQgLW1NTExMYwePZpLly7h7e3N4sWLsbOze2R/ERERREREEB0dDeQ+HszV1RVFUXBxccHLy4v169fj5+dHQkICR44cYefOncTHxxMREcGgQYOYMmUKAEuWLMHHx+eR+xs6dKjR67Nnz9KoUSMA4uLi6NixIxpN7h+Ltm3bMmnSJHVdvV7P8OHD+fzzz5k4ceIj96PX69Hr9eprnU4HgNZMwdy88I83q+y0ZorRZ/EXyY1plSkvBoNB/TouLo5u3bphZWXFiRMnjJatWbOGV155BUdHR7XdyclJvRzEYDCo7Q9uJ5C8FEDyUrDSyE1R+irzwi45ORl7e3ujtmnTpnH58mXc3NwICwujc+fOBAYGMmTIELZu3fq39qfT6QgNDWX48OFs3bqVzMxMdu3ahaOjI6dOneK7777jwIEDLF++nMDAwMcWdg+6ffs2S5cuVZ9Hq9PpeOmll9Tl1atXJzk5WX09e/ZsXnjhBd59993HFnZz5sxhxowZ+dqntMrBxia70DE+LWa1ySnvECosyY1plSEvkZGR6teKohAUFMRXX31F7969jd5jQkNDeffdd43WB/jf//6Xr5+oqKhSjvrJJHkxTfJSsJLMTUZGRqHXLfPCzmAwYG5unq/966+/xt3dnQ8++ADIHT1zcnLi2rVr1KlTp9j7Gzx4MNbW1jg4ONC7d2+2bNmiVr7p6emsWrUKe3t7hg0bxrx584rU98iRI3F3d8fb2xvIfTKFVqtVl1tbW6vfjNOnT/Pll19y/PjxQvU9efJkxowZo77W6XQ4OzvzyXEzsizz5+9ppTVTmNUmh+CjZuhznuzTaiVNcmNaZcrLqend8rX17NmTF198EXd3d+zs7Dh//jw3b95k8uTJWFpaGq2bN2LXvXt3DAYDUVFRdO3aNd96TzPJi2mSl4KVRm7yztoVRpkXdnZ2dqSkpBi1NW/enLS0NDw9PdU2R0dHtFotSUlJRSrsHq5qra2tjT4/yM3NTR09tLKyKvQ+AFatWsWePXuIi4tT22rUqMGNGzfU13fv3sXKygpFUfjwww/55JNPqFevXqH612q1RkVinpiJXYp9fV5lZDAYiIyM5NhUL3lzeYjkxrTKlpe9e/fyww8/qNf62traotFo0Gq1WFpasmXLFt58802Tz7POO/4H82BpaVkp8lLSJC+mSV4KVpK5KUo/ZX5XbMuWLTl79ix3794FICsri8TERIYNG8aFCxfU9ZKTk9Hr9TRo0OCR/Wk0GnJy/jqlcuzYsULHUr169SJGn+vo0aOMGjWKDRs2qNcKArzyyiscOnRIfX38+HEcHR1JSkpi//79jB8/Xr1DLSkpiebNm6uncYUQojheeOEFwsPDCQ8P5/LlywQFBeHp6am+v+3cuZM33nijfIMUQpSZMi/s3N3dcXNzw9/fnwsXLjBlyhTs7Ozw9fXl4MGDLFu2jMTERPz9/enTp49R4WSKo6MjCQkJ6HQ6bty4QUhISKnGf/36dXr27MmECRNo06YNaWlppKWlAdCrVy8OHDjA7t27MRgMhISE0K1bNxwdHUlMTCQ2Nlb9qFevHpGRkfTq1atU4xVCVG5169Zl06ZNLFy4kCZNmpCRkcHq1asBuHfvHocPH8bd3b2coxRClJUyL+zMzMzYvn07169fp2nTpkRHRxMZGYmzszM//vgjixYtolWrVtjY2LBy5crH9tepUyc8PT1p1qwZPXr0ICgoqFTjX79+PdeuXSM4OJhq1aqpHwC1atUiLCyM7t274+DgwNmzZ5kyZQoWFha4uLgYfVhYWODk5ETVqlVLNV4hROXXtWtX4uPj0el0bNy4kdq1awNQpUoV9Ho9L774osntXFxcUJQn/+5gIcRfyvwaO4D69euza9eufO0eHh7ExsYWuN0bb7yR72kNZmZm+U5nDhgwAMBo3byvH3yczpAhQ9SvC/sGFxAQQEBAQIHLP/roI7p168aZM2fo0KFDgYWbPHVCCCGEECWtXAq7iizv2jdTvLy82LBhw2P7cHV1xdXVtaRDE0IIIYR4JCnsHlKvXr0CRw1N3VUmhBBCCFFRSGH3kLzr4YQQQgghnjRlfvOEEEIIIYQoHVLYCSGEEEJUElLYCSGEEEJUElLYCSFEKUlJSeHw4cPcuXOnUO1CCPF3SWEnhBClYOPGjbi4uODn54eTkxMbN258ZDvAtm3baNiwIRYWFrRs2ZLTp0+XV/hCiCdUhSvsbt26RZ8+fbC1taVt27acOHGiUNtFR0eX+92siqIQEhJCo0aNqFWrFiNHjiQ9PT3fegaDgWbNmhEdHV32QQohSl1qaiojRowgJiaGkydPsmjRIsaPH19gO8Aff/zB0KFDmTt3LleuXOGFF17Az8+vnI9ECPGkqXCFna+vL9nZ2cTFxdGvXz98fHzIysoq77AKZcWKFSxcuJC1a9dy4MABjhw5wkcffZRvvZCQEE6dOlUOEQohyoJOp2PBggXqZOetW7fm1q1bBbYDnD59mrlz5/LOO+/g4OCAv78/x48fL7djEEI8mSrUPHbnz59nx44dJCcn4+DgwLhx45g3bx6HDx+mffv25R3eY61evZoxY8bQtm1bAGbMmEH//v2N1jl37hyffvppsUcX2835mSwL278baqWhNVcIaQtNp/+EPltT3uFUKJIb00ozLxfn9gDA2dmZQYMGAbkj9GFhYfTt27fAdoA333zTqK+zZ8/SqFGjEo1PCFH5VajC7tChQzRs2BAHBwcAzM3NCQgIwNrampiYGEaPHs2lS5fw9vZm8eLF2NnZPbK/iIgIIiIi1FOeFy9exNXVFUVRcHFxwcvLi/Xr1+Pn50dCQgJHjhxh586dxMfHExERwaBBg5gyZQoAS5YswcfH55H7u3nzJvXr11dfm5ubY25ubrTO8OHDmTRpEjt27HhkX3q9Hr1er77W6XQAaM0UzM3lod15tGaK0WfxF8mNaaWZF4PBYPQ6Li6Obt26YWVlxYkTJ9TlBbXnyczM5LPPPiMgICDfstKSt5+y2t+TQvJimuSlYKWRm6L0VaEKu+TkZOzt7Y3apk2bxuXLl3FzcyMsLIzOnTsTGBjIkCFD2Lp169/an06nIzQ0lOHDh7N161YyMzPZtWsXjo6OnDp1iu+++44DBw6wfPlyAgMDH1vYtW7dmm3btvH2228DuYVl165d1eUrV64kNTWVcePGPbawmzNnDjNmzMjXPqVVDjY22cU42sptVpuc8g6hwpLcmFYaeYmMjDR6rSgKQUFBfPXVV/Tu3ZuJEyc+sj3PmjVryMrKom7duvn6LG1RUVFlur8nheTFNMlLwUoyNxkZGYVet0IVdgaDId8IF8DXX3+Nu7s7H3zwAZA7eubk5MS1a9eoU6dOsfc3ePBgrK2tcXBwoHfv3mzZskWtitPT01m1ahX29vYMGzaMefPmPba/2bNn4+3tTfv27bl79y4nT54kJiYGgBs3bjB58mR++uknk8f4sMmTJzNmzBj1tU6nw9nZmY4dO1KzZs1iHnHlYzAYiIqKomvXrlhaWpZ3OBWK5Ma08shLz549efHFF3F3dzc602Cqfc+ePezatYt9+/bx0ksvlUl8ID8vBZG8mCZ5KVhp5CbvrF1hVKjCzs7OjpSUFKO25s2bk5aWhqenp9rm6OiIVqslKSmpSIXdwxWvtbW10ecHubm5qaOHVlZWheq/fv36nDp1ijNnzjBhwgTq1KlDhw4dAAgMDOT999+nRYsWhepLq9Wi1WrztVtaWsovkQmSl4JJbkwrzbzs3buXH374gdDQUABsbW3RaDQkJCSYbNdqtVhaWpKYmMg///lPFi1aVOj3ipImPy+mSV5Mk7wUrCRzU5R+KtRdsS1btuTs2bPcvXsXgKysLBITExk2bBgXLlxQ10tOTkav19OgQYNH9qfRaMjJ+et0y7FjxwodS/Xq1YsY/V/7rF69Ort37zYa5Vu3bh1ffPEFdnZ22NnZsX//ft58803mzp1brP0IISquF154gfDwcMLDw7l8+TJBQUF4enoW2F69enXu3bvHm2++Se/evenbty9paWmkpaWhKHKNpBCi8CpUYefu7o6bmxv+/v5cuHCBKVOmYGdnh6+vLwcPHmTZsmUkJibi7+9Pnz591JssCuLo6EhCQgI6nY4bN24QEhJSJsfxySef8Pbbb9OqVSu1LTExkRMnThAbG0tsbCxt2rRh+fLlJqdDEUI82erWrcumTZtYuHAhTZo0ISMjg9WrVxfYDrBr1y4SEhJYtmwZ1apVUz8uXbpUzkcjhHiSVKhTsWZmZmzfvh0/Pz+aNm1K8+bNiYyMxNnZmR9//JGAgADGjx+Pt7c3S5YseWx/nTp1wtPTk2bNmuHg4EBQUBADBw4s1WM4f/4869atIz4+3qj94elNrK2tqVOnzmPv7BVCPJm6du2a733gUe29e/eW0TkhxN9WoQo7yL1ObdeuXfnaPTw8iI2NLXC7N954g4sXLxq1mZmZsW7dOqO2AQMGABitm/d1RESE2jZkyBD1axcXl0K/4T7//POkpqY+dj156oQQQgghSlqFOhVb0SUlJanXyD388fBExEIIIYQQZa3CjdhVZPXq1Stw1NDGxqZsgxFCCCGEeIgUdkVgYWFR7EeBCSGEEEKUNjkVK4QQQghRSUhhJ4QQQghRSUhhJ4QQf0NKSgqHDx/mzp07pbqNEEIUhhR2QghRTBs3bsTFxQU/Pz+cnJzYuHEjANu2baNhw4ZYWFjQsmVLTp8+/dhthBCiJJRLYXfr1i369OmDra0tbdu25cSJE4XaLjo6ukLcvBAeHk7dunWxtLTEw8ODq1evqsv27t2Lm5sbtWrVYv78+UbbzZgxgxo1aqDVaunbt6/66DQhxJMnNTWVESNGEBMTw8mTJ1m0aBHjx4/njz/+YOjQocydO5crV67wwgsv4Ofn98hthBCipJRLYefr60t2djZxcXH069cPHx8fsrKyyiOUItu/fz/BwcGsWbOGxMREFEVh3LhxANy4cYNevXoxYMAADh06xNq1a9mzZw8Aa9euZe3atezcuZP4+HhOnz4tz4kV4gmm0+lYsGABzZs3B6B169bcunVL/d1+5513cHBwwN/fn+PHjz9yGyGEKCllPt3J+fPn2bFjB8nJyTg4ODBu3DjmzZvH4cOHad++fVmHU2Tnzp1j6dKldOnSBYChQ4cSGhoK5BZv9erVIzg4GI1Gw9SpU1mxYgUdO3bk8uXLrFq1irZt2wLw7rvv8uuvvxZ5/+3m/EyWhW3JHdATTmuuENIWmk7/CX22przDqVAkN6aVRF4uzu2Bs7MzgwYNAsBgMBAWFkbfvn158803jdY9e/YsjRo1AihwGyGEKCllXtgdOnSIhg0b4uDgAIC5uTkBAQFYW1sTExPD6NGjuXTpEt7e3ixevPixz1KNiIggIiJCfUTXxYsXcXV1RVEUXFxc8PLyYv369fj5+ZGQkMCRI0fUUbOIiAgGDRrElClTAFiyZAk+Pj6P3N/QoUONXj/4ph0XF0fHjh3RaHL/WLRt25ZJkyYBqJ9NbWeKXq9Hr9err3U6HQBaMwVzc3meZB6tmWL0WfxFcmNaSeTFYDCoX8fFxdGtWzesrKw4ceKE0bLMzEw+++wzAgICCr1NecmLoSLEUpFIXkyTvBSsNHJTlL7KvLBLTk7G3t7eqG3atGlcvnwZNzc3wsLC6Ny5M4GBgQwZMoStW7f+rf3pdDpCQ0MZPnw4W7duJTMzk127duHo6MipU6f47rvvOHDgAMuXLycwMPCxhd2Dbt++zdKlS9Xn0ep0Ol566SV1efXq1UlOTs633e+//86WLVv47bffCux7zpw5zJgxI1/7lFY52NhkFzrGp8WsNjnlHUKFJbkx7e/kJTIyUv1aURSCgoL46quv6N27NxMnTlSXrVmzhqysLOrWrVvobcpbVFRUeYdQIUleTJO8FKwkc5ORkVHodcu8sDMYDJibm+dr//rrr3F3d+eDDz4AckfPnJycuHbtGnXq1Cn2/gYPHoy1tTUODg707t2bLVu2qJVveno6q1atwt7enmHDhjFv3rwi9T1y5Ejc3d3x9vYGcp9ModVq1eXW1tb5vhk5OTkMGzYMPz8/mjRpUmDfkydPZsyYMeprnU6Hs7Mznxw3I8syf/6eVlozhVltcgg+aoY+R043PkhyY1pJ5OXU9G752nr27MmLL76Iu7s7dnZ27Nmzh127drFv3z6jf/getU15MhgMREVF0bVrVywtLcs1lopE8mKa5KVgpZGbvLN2hVHmhZ2dnR0pKSlGbc2bNyctLQ1PT0+1zdHREa1WS1JSUpEKu4cLKWtra6PPD3Jzc1NHD62srAq9D4BVq1axZ88e4uLi1LYaNWpw48YN9fXdu3fz9Ttr1ixu376tXpdXEK1Wa1Qk5omZ2IWaNWsWKdbKzGAwEBkZybGpXvLm8hDJjWkllZe9e/fyww8/qL/Ltra2aDQatFotf/75J//85z9ZtGgRLVq0KNQ2FeV7ZGlpWWFiqUgkL6ZJXgpWkrkpSj9lfldsy5YtOXv2rDrVR1ZWFomJiQwbNowLFy6o6yUnJ6PX62nQoMEj+9NoNOTk/HVK5dixY4WOpXr16kWMPtfRo0cZNWoUGzZsUK8VBHjllVc4dOiQ+vr48eM4Ojqqr7///nvmz5/P5s2bsbGxKda+hRAVwwsvvEB4eDjh4eFcvnyZoKAgPD09sbS05M0336R379707duXtLQ00tLSUBSlwG2K+14khBAPK/PCzt3dHTc3N/z9/blw4QJTpkzBzs4OX19fDh48yLJly0hMTMTf358+ffoYFU6mODo6kpCQgE6n48aNG4SEhJRq/NevX6dnz55MmDCBNm3aqG/aAL169eLAgQPs3r0bg8FASEgI3brlnrI5ffo0AwYM4IsvvsDZ2Zm0tLQinTMXQlQsdevWZdOmTSxcuJAmTZqQkZHB6tWr2bVrFwkJCSxbtoxq1aqpH5cuXSpwGyGEKCllXtiZmZmxfft2rl+/TtOmTYmOjiYyMhJnZ2d+/PFHFi1aRKtWrbCxsWHlypWP7a9Tp054enrSrFkzevToQVBQUKnGv379eq5du0ZwcLDRmzZArVq1CAsLo3v37jg4OHD27Fn1jtvw8HDS09Px9fVVtynouhshxJOha9euxMfHo9Pp2LhxI7Vr16Z3794oipLvI29ydVPbCCFESdEoiiJzIZSwxMREzpw5Q4cOHahatWqJ9KnT6XjmmWe4efOmXGP3gLzrpbp37y7XeTxEcmOa5MU0yYtpkhfTJC8FK43c5NUAqampj710o8xvnqjokpKS1FnhH+bl5cWGDRse24erqyuurq4lHZoQQgghxCNJYfeQevXqERsba3KZ3PAghBBCiIpMCruHWFhYqNfCCCGEEEI8Scr85gkhhBBCCFE6pLATQgghhKgkpLATQgghhKgkpLATQohiSElJ4fDhw9y5c6e8QxFCCFWFK+xu3bpFnz59sLW1pW3btpw4caJQ20VHR1eImx7Cw8OpW7culpaWeHh4cPXqVXXZ3r17cXNzo1atWsyfP78coxRC/B0bN27ExcUFPz8/nJyc2LhxIwDbtm2jYcOGWFhY0LJlS06fPq1uc+rUKV555RWeffZZxo8fj0whKoQoDRWusPP19SU7O5u4uDj69euHj48PWVlZ5R1Woezfv5/g4GDWrFlDYmIiiqIwbtw4AG7cuEGvXr0YMGAAhw4dYu3atezZs6ecIxZCFFVqaiojRowgJiaGkydPsmjRIsaPH88ff/zB0KFDmTt3LleuXOGFF17Az88PAL1eT8+ePXn55Zc5evQoCQkJRERElO+BCCEqpQo13cn58+fZsWMHycnJODg4MG7cOObNm8fhw4dp3759eYf3WOfOnWPp0qV06dIFgKFDhxIaGgrA2rVrqVevHsHBwWg0GqZOncqKFSvo2LFjkfbRbs7PZFnYlnjsTyqtuUJIW2g6/Sf02ZryDqdCkdyY9nfycnFuD3Q6HQsWLFAnMm/dujW3bt3i9OnTzJ07l3feeQcAf39/evToAcCOHTtITU1l/vz52NjYMHv2bEaOHMnQoUNL9uCEEE+9ClXYHTp0iIYNG+Lg4ACAubk5AQEBWFtbExMTw+jRo7l06RLe3t4sXrwYOzu7R/YXERFBREQE0dHRAFy8eBFXV1f1uY1eXl6sX78ePz8/EhISOHLkCDt37iQ+Pp6IiAgGDRqkPut1yZIl+Pj4PHJ/D79Jnz17lkaNGgEQFxdHx44d0Why/5C0bduWSZMmFdiXXq9Hr9err3U6HQBaMwVzczmFk0drphh9Fn+R3Jj2d/JiMBioU6cO77zzDgaDAYPBwGeffUbv3r3p1q2bug5AQkICzz//PAaDgd9++4127dphaWmJwWDAzc2NhIQEdd2KIC+WihRTRSB5MU3yUrDSyE1R+qpQhV1ycjL29vZGbdOmTePy5cu4ubkRFhZG586dCQwMZMiQIWzduvVv7U+n0xEaGsrw4cPZunUrmZmZ7Nq1C0dHR06dOsV3333HgQMHWL58OYGBgY8t7B50+/Ztli5dyrp169R9vfTSS+ry6tWrk5ycXOD2c+bMYcaMGfnap7TKwcYmuwhH+XSY1SanvEOosCQ3phUnL5GRkerXiYmJTJ06FQsLC7744gujZQaDgf/85z/06tWLyMhI4uLi0Gg0RutkZ2fz7bffltjzpEtKVFRUeYdQIUleTJO8FKwkc5ORkVHodStUYWcwGDA3N8/X/vXXX+Pu7s4HH3wA5I6eOTk5ce3aNerUqVPs/Q0ePBhra2scHBzo3bs3W7ZsUavi9PR0Vq1ahb29PcOGDWPevHlF6nvkyJG4u7vj7e0N5D7RQqvVqsutra0f+Y2aPHkyY8aMUV/rdDqcnZ355LgZWZb5c/S00popzGqTQ/BRM/Q5crrxQZIb0/5OXk5N76Z+rSgKr732GuPGjWPz5s1888036rJ///vf1K5dm/nz52Npacm+ffvIysqie/fu6jrVq1enffv2ODo6/v2DKgEGg4GoqCi6du0qD3V/gOTFNMlLwUojN3ln7QqjQhV2dnZ2pKSkGLU1b96ctLQ0PD091TZHR0e0Wi1JSUlFKuweLqSsra2NPj/Izc1NHT20srIq9D4AVq1axZ49e4iLi1PbatSowY0bN9TXd+/efWS/Wq3WqBDMEzOxCzVr1ixSPJWZwWAgMjKSY1O95M3lIZIb00oyL+3atWP16tU899xzpKenY2dnxy+//MKXX37J//3f/6nPl65duzanTp0y2t/du3extbWtcN8bS0vLChdTRSB5MU3yUrCSzE1R+in2XbFRUVEoisL9+/dZsGABn376aZGGCk1p2bIlZ8+e5e7duwBkZWWRmJjIsGHDuHDhgrpecnIyer2eBg0aPLI/jUZDTs5fp1uOHTtW6FiqV69exOhzHT16lFGjRrFhwwb1WkGAV155hUOHDqmvjx8/XmH+UxdCFN7evXsZP368+trKygqNRoOZmRmJiYkMGDCARYsWGV168fDvf2JiInq9nho1apRp7EKIyq9Yhd2oUaPw8/MjJyeHMWPGsHLlStasWcOQIUP+VjDu7u64ubnh7+/PhQsXmDJlCnZ2dvj6+nLw4EGWLVtGYmIi/v7+9OnTx6hwMsXR0ZGEhAR0Oh03btwgJCTkb8X3ONevX6dnz55MmDCBNm3akJaWRlpaGgC9evXiwIED7N69G4PBQEhIiHqxtRDiyfHCCy8QHh5OeHg4ly9fJigoCE9PTywtLXnzzTfp3bs3ffv2VX//FUXh9ddfR6fTsXLlSgBmz55Nly5dTF56IoQQf0exCrt169YRGRmJmZkZmzZtIioqiq1bt/LTTz/9vWDMzNi+fTvXr1+nadOmREdHExkZibOzMz/++COLFi2iVatW2NjYqG+Qj9KpUyc8PT1p1qwZPXr0ICgo6G/F9zjr16/n2rVrBAcHU61aNfUDoFatWoSFhdG9e3ccHBw4e/asesetEOLJUbduXTZt2sTChQtp0qQJGRkZrF69ml27dpGQkMCyZcuMfv8vXbqEhYUFy5cv5+OPP6ZWrVps27atyNftCiFEYRTrGjtzc3MUReH48ePUqVMHe3t7YmNjTV6rVlT169dn165d+do9PDyIjY0tcLs33niDixcvGrWZmZmpd6XmGTBgAIDRunlfPzhh6IOjjy4uLoWaJT4gIICAgIACl3/00Ud069aNM2fO0KFDhwp3N5wQonC6du1KfHy8UVvv3r0f+T7Rq1cv/vjjD44dO8arr74q18oKIUpFsQq7IUOG8Prrr6PRaJgwYQK///47gwYN4q233irp+CqUpKQkdVLSh3l5ebFhw4bH9uHq6oqrq2tJhyaEeALUqVNHnbRYCCFKQ7EKu5CQEDp37kyVKlV4/fXXSUxMJDAwsNLPol6vXr0CRw3z7n4TQgghhCgvxZ7u5MEL/11dXdU55iozCwsLXFxcyjsMIYQQQgiTij3dybp16+jfvz/t27fn3LlzvPPOO9y8ebMkYxNCCCGEEEVQrMLu3//+NxMmTMDV1ZW4uDjMzHK7GT58eIkGJ4QQQgghCq9Yp2KXLVvGzz//TLNmzfjyyy+xtLRk/vz5NGnSpKTjE0IIIYQQhVSsETs7OzsuX75s1HblypXHThgshBBPkpSUFA4fPsydO3fKOxQhhCiUYhV2U6ZMoW/fvgwYMAC9Xk9YWBgDBgwgODi4pOMTQohysXHjRlxcXPDz88PJyYmNGzeqy27evImrq2u+uTNPnTrFK6+8wrPPPsv48eMLNf+lEEKUpGIVdoMHDyYqKopq1arxxhtvkJ6ezurVq/nnP//5twO6desWffr0wdbWlrZt23LixIlCbRcdHV2h7lg1GAw0a9aM6OhotW3GjBnUqFEDrVZL37591WfiCiEqltTUVEaMGEFMTAwnT55k0aJF6vNhb968yZtvvpmvqNPr9fTs2ZOXX36Zo0ePkpCQYDTpuRBClIVi3xX7+uuvEx4eTmRkJOHh4fzjH/8okYB8fX3Jzs4mLi6Ofv364ePjQ1ZWVon0XZZCQkI4deqU+nrt2rWsXbuWnTt3Eh8fz+nTp5k7d245RiiEKIhOp2PBggXqhOStW7fm1q1bAPTv35+BAwfm22bHjh2kpqYyf/58nnvuOWbPns2KFSvKNG4hhCjWzROnT5/GxcWFKlWqlGgw58+fZ8eOHSQnJ+Pg4MC4ceOYN28ehw8fpn379iW6r9J07tw5Pv30U6MRxMuXL7Nq1Sratm0LwLvvvsuvv/5aYB96vR69Xq++1ul0ALw+bzdZlralE/gTSGumMKsNvDxzJ/ocTXmHU6FIbkx7XF5OTe9GnTp1eOeddzAYDBgMBj777DN69+6NwWBg8eLFuLq6EhAQoC4H+O2332jXrh2WlpYYDAbc3NxISEhQl1d0eXE+KfGWFcmLaZKXgpVGborSV7EKu86dO7Nu3TreeOON4mxeoEOHDtGwYUP1Jgxzc3MCAgKwtrYmJiaG0aNHc+nSJby9vVm8eDF2dnaP7C8iIoKIiAj1dOjFixdxdXVFURRcXFzw8vJi/fr1+Pn5kZCQwJEjR9QRtYiICAYNGsSUKVMAWLJkCT4+PoU6juHDhzNp0iR27Nihtk2aNMlonbNnz9KoUaMC+5gzZw4zZszI1z6lVQ42NtmFiuNpMqtNTnmHUGFJbkwrKC+RkZHq14mJiUydOhULCwu++OILddnp06cB2LNnj/p+FRcXh0ajMdo+Ozubb7/99ol6LnRUVFR5h1AhSV5Mk7wUrCRzk5GRUeh1i1XY+fr6snLlyhIv7JKTk7G3tzdqmzZtGpcvX8bNzY2wsDA6d+5MYGAgQ4YMYevWrX9rfzqdjtDQUIYPH87WrVvJzMxk165dODo6curUKb777jsOHDjA8uXLCQwMLFRht3LlSlJTUxk3bpxRYfeg33//nS1btvDbb78V2M/kyZMZM2aMUazOzs58ctyMLEvzoh9sJZU7+pJD8FEzGZV6iOTGtMfl5dT0v56qoygKr732GuPGjWPz5s188803Rut27NhRHZnft28fWVlZdO/eXV1evXp12rdvj6OjY+kcTAkyGAxERUXRtWtXLC0tyzucCkPyYprkpWClkZu8s3aFUazCrkuXLgQHB9OjRw8+/vhjbG3/OjX4+uuvF6dLIDcZ5ub5i5avv/4ad3d39bFlS5YswcnJiWvXrlGnTp1i72/w4MFYW1vj4OBA79692bJlizrcmZ6ezqpVq7C3t2fYsGHMmzfvsf3duHGDyZMn89NPP5k8DoCcnByGDRuGn5/fI+f902q1aLXafO0xE7tQs2bNQh5h5WcwGIiMjOTYVC95c3mI5Ma0oualXbt2rF69mueee4709HSjMwWWlpZqH7Vr1+bUqVNGfd69exdbW9snKv8PHpP4i+TFNMlLwUoyN0Xpp1iFnZ+fHwBXr15lxIgRartGo+HChQvF6RLInR8vJSXFqK158+akpaXh6emptjk6OqLVaklKSipSYffwUKa1tbXR5we5ubmpo4dWVlaF6j8wMJD333+fFi1aFLjOrFmzuH37NqGhoYUNWwhRxvbu3csPP/yg/p5aWVmh0WjUp+yY8sorr7Bs2TL1dWJiInq9nho1apR6vEIIkadYhV1iYmJJxwFAy5YtOXv2LHfv3qVatWpkZWWRmJjIxIkTiYmJUddLTk5Gr9fToEGDR/an0WjIyfnrOppjx44VOpbq1asXOf5169ZRrVo1Fi1aBEBaWhpvvvkmU6ZMYdKkSXz//ffMnz+f//u//8PGxqbI/QshysYLL7xAeHg4jRo1wtvbmylTpuDp6fnI94XXX38dnU7HypUrGTp0KLNnz6ZLly4Fjt4LIURpKPZ0J6XB3d0dNzc3/P39uXDhAlOmTMHOzg5fX18OHjzIsmXLSExMxN/fnz59+jz2SReOjo4kJCSg0+m4ceMGISEhpRp/YmIiJ06cIDY2ltjYWNq0acPy5cv56KOPOH36NAMGDOCLL77A2dmZtLS0Il0MKYQoO3Xr1mXTpk0sXLiQJk2akJGRwerVqx+5jYWFBcuXL+fjjz+mVq1abNu2rVCXcAghREkq1oidq6srGo3pi7H/zqlYMzMztm/fjp+fH02bNqV58+ZERkbi7OzMjz/+SEBAAOPHj8fb25slS5Y8tr9OnTrh6elJs2bNcHBwICgoyOT8UyXl4QmSra2tqVOnDnZ2dsyYMYP09HR8fX3x9fUFoEGDBvkmORVCVAxdu3YlPj6+wOWmnirRq1cv/vjjD44dO8arr74q18MKIcpcsQq7B2dTz8jI4OjRo4SHhzN16tS/HVD9+vXZtWtXvnYPDw9iY2ML3O6NN97IVySZmZmxbt06o7YBAwYAGK2b9/WDxzVkyBD1axcXl2I9GujBp06EhYURFhZW5D6EEE+WOnXq0KNHj/IOQwjxlCpWYefh4WH02tvbmwEDBvD++++rd65WRklJSepM9A/z8vJiw4YNZRyREEIIIcRfilXYmeLi4sKVK1dKqrsKqV69egWOGsrNEEIIIYQob8Uq7IYOHWp0jV1OTg7Hjh3j+eefL7HAKiILC4t819EJIYQQQlQUxSrsHi5uNBoNHTp0UK9fE0IIIYQQZa9Yhd20adNKOg4hhBBCCPE3FWseu2+//ZbsbOMH0e/bt49//vOfJRKUEEKUt5SUFA4fPsydO3fKOxQhhCi0YhV2AwYMID093ajtueeeY+PGjSUSlBBClKeNGzfi4uKCn58fTk5O6nvbqVOneOWVV3j22WcZP3680TRIs2bNwsHBgapVq9KrVy9u3rxZXuELIZ5iRSrskpKSSEpKQlEULl++rL6+dOkS27Ztw8nJqVD93Lp1iz59+mBra0vbtm05ceJEobaLjo6uEDcvhIeHU7duXSwtLfHw8ODq1atGy8+fP2/y+ZDyxi9ExZeamsqIESOIiYnh5MmTLFq0iPHjx6PX6+nZsycvv/wyR48eJSEhQZ37MiYmhm+++YaYmBhiY2PJzs5mzJgx5XsgQoinUpEKOxcXF/WpE82aNcPFxQUXFxcaNmzIggULWLp0aaH68fX1JTs7m7i4OPr164ePjw9ZWVnFOoCytn//foKDg1mzZg2JiYkoisK4cePU5RcuXKB79+75Tt/IG78QTwadTseCBQvUOStbt27NrVu32LFjB6mpqcyfP5/nnnuO2bNns2LFCgCOHDlC9+7dady4Mc8//zwDBw7k/Pnz5XkYQoinVJFunsjJyQFyn+hw584dnnnmmSLv8Pz58+zYsYPk5GQcHBwYN24c8+bN4/Dhw7Rv377I/ZW1c+fOsXTpUrp06QLkTv0SGhqqLu/Zsycffvgh48ePN9ruwTd+gIEDB7Jo0aIi77/dnJ/JsrD9G0dQuWjNFULaQtPpP6HPNv2Yu6eV5Ma0gvJycW7u0yKcnZ0ZNGgQAAaDgbCwMPr27UtcXByvvvqqOmdl8+bNSUhIAKBJkyYsXbqUjz76iKpVq7JixQq6du1axkcmhBDFvCu2cePGWFgUb27jQ4cO0bBhQxwcHAAwNzcnICAAa2trYmJiGD16NJcuXcLb25vFixdjZ2f3yP4iIiKIiIhQH9918eJFXF1dURQFFxcXvLy8WL9+PX5+fiQkJHDkyBF27txJfHw8ERERDBo0iClTpgCwZMkSfHx8Hrm/oUOHGr0+e/YsjRo1Ul//8MMPaDSafIVdUd/49Xo9er1efa3T6QDQmimYmxf98WaVldZMMfos/iK5Ma2gvBgMBqPXcXFxdOvWDSsrK06cOMHs2bNp0KCB0Xrm5uZcv36dLl260LBhQ5577jkA2rRpw9ixY/P1WZHlxfokxVwWJC+mSV4KVhq5KUpfxarOTp8+XZzNAEhOTsbe3t6obdq0aVy+fBk3NzfCwsLo3LkzgYGBDBkyhK1btxZ7X5BbEIWGhjJ8+HC2bt1KZmYmu3btwtHRkVOnTvHdd99x4MABli9fTmBg4GMLuwfdvn2bpUuXGj2P1tXVNd8zayH3sWvPPfec+sb/yiuvMGnSpAL7njNnDjNmzMjXPqVVDjY22Sa2eLrNapNT3iFUWJIb0x7OS2RkpNFrRVEICgriq6++onfv3tSpU4fs7Gyj9RRF4YcffuDs2bMkJCTwxRdfUL16dVatWkW3bt0e+TteUUVFRZV3CBWS5MU0yUvBSjI3GRkZhV63WIVdRkYGixcv5uzZs+q0J4qiEBsby/Hjxx+5rcFgwNzcPF/7119/jbu7u/qs2SVLluDk5MS1a9eoU6dOccIEYPDgwVhbW+Pg4EDv3r3ZsmWLWvmmp6ezatUq7O3tGTZsGPPmzStS3yNHjsTd3R1vb+/Hrrtp0yaSkpJISEigVq1aTJgwgffee4/NmzebXH/y5MlG1+DpdDqcnZ355LgZWZb58/e00popzGqTQ/BRM/Q5crrxQZIb0wrKy6np3Uyu37NnT1588UU++eQT4uPj6d69u7osKysLLy8vtm3bxtixYxk+fDgA3bt3p3bt2ri7uz/2rENFYTAYiIqKomvXrlhaWpZ3OBWG5MU0yUvBSiM3eWftCqNYhd3gwYO5efMmBoMBCwsLXn31VZYsWYKfn99jt7WzsyMlJcWorXnz5qSlpeHp6am2OTo6otVqSUpKKlJh93BVa21tbfT5QW5uburooZWVVaH3AbBq1Sr27NlDXFxcodZfu3Yt/v7+uLm5AbBgwQI1F6be+LVaLVqtNl97zMQu1KxZs0ixVmYGg4HIyEiOTfWSN5eHSG5Me1xe9u7dyw8//KBeO2tra4tGo6Fp06Z89dVX6jaJiYno9Xr1spJbt26py27dugXkXo/8pOXe0tLyiYu5LEheTJO8FKwkc1OUfoo1j11UVBRr165l5syZWFlZMW/ePL788kvi4+Mfu23Lli05e/Ysd+/eBXL/401MTGTYsGFcuHBBXS85ORm9Xk+DBg0e2Z9Go1Fv6gA4duxYoY+jevXqhV73QUePHmXUqFFs2LBBfVN/nJycHK5fv66+vnbtGkC+iZ6FEOXrhRdeIDw8nPDwcC5fvkxQUBCenp50794dnU7HypUrAZg9ezZdunTB3NycDh06EB4ezpdffsmqVavo378/7u7u8k+YEKLMFauws7Oz48yZM7z66qvExcWRk5NDx44dOXTo0GO3dXd3x83NDX9/fy5cuMCUKVOws7PD19eXgwcPsmzZMhITE/H396dPnz6PLZwcHR1JSEhAp9Nx48YNQkJCinNIhXb9+nV69uzJhAkTaNOmDWlpaaSlpT12O3njF+LJULduXTZt2sTChQtp0qQJGRkZrF69GgsLC5YvX87HH39MrVq12LZtm3r5xqhRo+jfvz+zZs3iww8/5JlnnuHrr78u5yMRQjyNinUqdsqUKXh7e3Pt2jXat2+Pp6cnOTk5NG3a9LHbmpmZsX37dvz8/GjatCnNmzcnMjISZ2dnfvzxRwICAhg/fjze3t4sWbLksf116tQJT09PmjVrhoODA0FBQQwcOLA4h1Uo69ev59q1awQHBxMcHKy2PzgDvSmjRo3i8uXLzJo1i5s3b/Laa6+p//kLISqWrl27mjwD0atXL/744w+OHTvGq6++qv5jptVqWbhwIQsXLizrUIUQwohGeVxFUoCEhASee+457t27x8KFC7l79y6BgYGFfvqEKBqdTsczzzzDzZs3ZZTvAXnXS3Xv3l2u83iI5MY0yYtpkhfTJC+mSV4KVhq5yasBUlNTH3sZWfEmowNeeuklIPc/1WnTphW3mwonKSlJnXH+YV5eXmzYsKGMIxJCCCGEKJxiFXYGg4HQ0FC2bdvGn3/+ya5duxg6dCjr169X52l7UtWrV4/Y2FiTy/JmnBdCCCGEqIiKVdiNGDGCX3/9FX9/f8aPH4+NjQ3u7u4MHz6c3bt3l3SMZcrCwgIXF5fyDkMIIYQQosiKdVfspk2b2Lx5M8OHD8fc3Bxzc3MmTpzI4cOHSzo+IYQQQghRSMUq7JydnYmJiVFfazQa4uPjcXV1LbHAhBBCCCFE0RTrVGxISAh9+vQhPDycjIwMxowZw759+1i9enVJxyeEEEIIIQqp0CN2v/zyi/qEBy8vL+Lj4+nZsyfvv/8+rVq14uDBg0aPBBNCiCdZSkoKhw8f5s6dO+UdihBCFFqhC7uuXbsaPWHh/v37jB8/nsWLFxMUFETDhg0LvdNbt27Rp08fbG1tadu2LSdOnCjUdtHR0RXmxobz589To0aNfO2zZs3CwcGBqlWr0qtXL27evKkuCw8Pp27dulhaWuLh4cHVq1fLMmQhRCFt3LgRFxcX/Pz8cHJyYuPGjQCcOnWKV155hWeffZbx48cbTUz+qN99IYQoK4Uu7B6ex7hDhw7FLkx8fX3Jzs4mLi6Ofv364ePjQ1ZWVrH6Kg8XLlyge/fu+f6Tj4mJ4ZtvviEmJobY2Fiys7MZM2YMAPv37yc4OJg1a9aQmJiIoiiMGzeuPMIXQjxCamoqI0aMICYmhpMnT7Jo0SLGjx+PXq+nZ8+evPzyyxw9epSEhAQiIiKAR//uCyFEWSr0NXYajcbodTEfWMH58+fZsWMHycnJODg4MG7cOObNm8fhw4dp3759sfosaz179uTDDz9k/PjxRu1Hjhyhe/fuNG7cGICBAweyaNEiAM6dO8fSpUvp0qULAEOHDiU0NLTI+24352eyLGz/5hFUHlpzhZC20HT6T+izNY/f4CkiuTGtoLxcnNsDyJ3hfcGCBepE5a1bt+bWrVvs2LGD1NRU5s+fj42NDbNnz2bkyJEMHTr0kb/7QghRlop088SDxZ1Go8lX7BXGoUOHaNiwIQ4ODgCYm5sTEBCAtbU1MTExjB49mkuXLuHt7c3ixYuxs7N7ZH8RERFEREQQHR0NwMWLF3F1dUVRFFxcXPDy8mL9+vX4+fmRkJDAkSNH2LlzJ/Hx8URERDBo0CCmTJkCwJIlS/Dx8XnsMfzwww9oNJp8hV2TJk1YunQpH330EVWrVmXFihV07doVyC3kHnT27FkaNWpU4D70ej16vV59rdPpANCaKZibF6+oroy0ZorRZ/EXyY1pBeXFYDAAUKdOHd555x0MBgMGg4HPPvuM3r1789tvv9GuXTssLS0xGAy4ubmRkJCAwWCgcePGLF26lPfff5+qVauyfPlyOnXqpPb5JMiL9UmKuSxIXkyTvBSsNHJTlL4KXdgpisI//vEPzM3NgdxCw9vbGysrK6P1fvvtt0f2k5ycjL29vVHbtGnTuHz5Mm5uboSFhdG5c2cCAwMZMmQIW7duLWyIJul0OkJDQxk+fDhbt24lMzOTXbt24ejoyKlTp/juu+84cOAAy5cvJzAwsFCFnaurKxcvXszX7u3tzXPPPac+feOVV15h0qRJ+da7ffs2S5cuZd26dQXuY86cOcyYMSNf+5RWOdjYZD82xqfNrDY55R1ChSW5Me3hvERGRhq9TkxMZOrUqVhYWPDFF1/w7bffotFojNbLzs7m22+/pWrVqlSrVo0XX3wRgEaNGtG8efN8fT4JoqKiyjuECknyYprkpWAlmZuMjIxCr1vowm7lypXFCuZhBoNBLQ4f9PXXX+Pu7s4HH3wA5I6eOTk5ce3aNerUqVPs/Q0ePBhra2scHBzo3bs3W7ZsUSvf9PR0Vq1ahb29PcOGDWPevHnF3g/kTtyclJREQkICtWrVYsKECbz33nts3rzZaL2RI0fi7u6Ot7d3gX1NnjzZ6BodnU6Hs7Mznxw3I8syf/6eVlozhVltcgg+aoY+R043PkhyY1pBeTk1vZvReoqi8NprrzFu3Dg2b95Mo0aNyMrKonv37uo61atXp3379vzf//0f9+7dIy4ujlq1ajF58mTWr1/Pt99+W2bH9XcZDAaioqLo2rWrPNT9AZIX0yQvBSuN3OSdtSuMQhd2vr6+xQrmYXZ2dqSkpBi1NW/enLS0NKPpUhwdHdFqtSQlJRWpsHu4qrW2tjb6/CA3Nzd19PDhkcfiWLt2Lf7+/ri5uQGwYMEC9XjzTimvWrWKPXv2EBcX98i+tFotWq02X3vMxC7UrFnzb8daWRgMBiIjIzk21UveXB4iuTGtKHlp164dq1ev5rnnnmPOnDmcOnXKaJu7d+9ia2vLN998w4gRI9Tr8j7//HPs7OxIT09/7OUkFY2lpaX8vJggeTFN8lKwksxNUfop1pMn/o6WLVty9uxZ7t69C0BWVhaJiYkMGzaMCxcuqOslJyej1+tp0KDBI/vTaDTq/HoAx44dK3Qs1atXL2L0j5aTk8P169fV19euXQNyT9cAHD16lFGjRrFhwwb1GkMhRMWyd+9eo+tnrays0Gg0uLm5cejQIbU9MTERvV5PjRo1Hvu7L4QQZaXMCzt3d3fc3Nzw9/fnwoULTJkyBTs7O3x9fTl48CDLli0jMTERf39/+vTp89gCyNHRkYSEBHQ6HTdu3CAkJKSMjiS/Dh06EB4ezpdffsmqVavo378/7u7u1KxZk+vXr9OzZ08mTJhAmzZtSEtLM5oXUAhRMbzwwguEh4cTHh7O5cuXCQoKwtPTk+7du6PT6dTLUmbPnk2XLl0wNzd/5O++EEKUpTIv7MzMzNi+fTvXr1+nadOmREdHExkZibOzMz/++COLFi2iVatW2NjYFOq6vk6dOuHp6UmzZs3o0aMHQUFBZXAUpo0aNYr+/fsza9YsPvzwQ5555hm+/vprANavX8+1a9cIDg6mWrVq6ocQomKpW7fu/2vv3sOirNPHj7+H07AgOJo5KCCgkY4haXla/LaWpxRTETfTLEUXNdwUtrTSNDO3PLCGbj80FRMPaScVqNAVS/KQq5euQIpRBIJIfD2kDmg7DPD8/vDLkyMDgimDcL+ua66Z5/Oc7s/tPMPtc+Szzz5jxYoVPPTQQ1y7do2NGzfi4OBAXFwcL774Iq1atSIxMVE9L7embV8IIeqTRrndG9KJemU0GmnevDkXLlyQvQA3qDxfKjg4WM7zuInkxrrfm5eioiKOHTtG7969G9W2KN8X6yQv1kleqnc3clNZA1y5cuWWp5HV+x67hi4/Px+dTmf1NWbMGFuHJ4SwMQ8PD4YOHdqoijohRONRpxsUNwVt27YlLS3N6jgXF5f6DUYIIYQQog6ksLuJg4MDvr6+tg5DCCGEEKLO5FCsEEIIIUQjIYWdEEIIIUQjIYWdEEIIIUQjIYWdEEJYcfnyZQ4fPsylS5dsHYoQQtSaTQq7ixcvEhISgqurKz179iQjI6NW86WmpjaYCxuys7Np2bJllfbhw4ej0WjU14ABA9RxCxcuRK/X06xZM4YPH86FCxfqM2QhRC19+umn+Pr6Eh4ejpeXF59++ikAJ06coEePHrRo0YJZs2Zx421AZfsWQjQENinsJkyYQHl5Oenp6YwaNYrQ0FDKyspsEcptycnJITg42Or/5I8ePcp3333HpUuXuHTpEomJiQDs27ePjz/+mH379pGWlkZ5eTkvvfRSfYcuhLiFK1euMG3aNPbt28d3331HbGwss2bNwmQyMWzYMB599FGOHj1KZmYm8fHxgGzfQoiGo94Lu+zsbHbu3ElcXBwPPPAAM2fO5JdffuHw4cP1HcptGzZsGFOmTKnSfvbsWRRFISAgQL2psaurKwBHjhwhODiYjh078sADD/Dss8+SnZ1d36ELIW7BaDSyfPlyAgMDAXjkkUe4ePEiO3fu5MqVK7z77rt06NCBd955h3Xr1gGyfQshGo56v4/doUOHaN++PXq9HgB7e3siIyNxdnZm3759zJgxg7y8PIYMGcLKlSvR6XQ1Li8+Pp74+HhSU1MBOH36NH5+fiiKgq+vL4MHD2br1q2Eh4eTmZnJkSNH2LVrFydPniQ+Pp5x48Yxd+5cAFatWkVoaOgt+/DFF1+g0WiYNWuWRfuRI0coLy/Hy8uLS5cuMWzYMFatWkWLFi146KGHWL16NS+88ALNmjVj3bp1DBw4sNp1mEwmTCaTOmw0GgH405I9lDm63jLGpkJrp7CwOzz61i5MFRpbh9OgSG6sqy4vJ958Erj+ZInRo0djNpsxm80sW7aMESNG8J///IdevXrh6OiI2WzGYDCQmZmJ2WymY8eOrF69mr/85S80a9aMuLg4+vXrh9lstlU366wy1nsp5vogebFO8lK9u5Gbuiyr3gu7wsJCWrdubdE2f/58zpw5g8FgICYmhv79+xMVFUVYWBgJCQm/a31Go5Ho6GimTp1KQkICpaWl7N69G09PT06cOMH27ds5ePAgcXFxREVF1aqw8/Pz4/Tp01Xav//+ex5++GH+8Y9/YGdnR3h4OLNnz+b9999nyJAhdOjQgQ4dOgDQo0cPXnvttWrXsWjRIhYsWFClfW63ClxcymufgCZiYfcKW4fQYElurLs5L8nJyRbDubm5vPHGGzg4OPDee+/xySefoNFoLKYrLy/nk08+oVmzZri5udGpUycA/P39CQwMrLLMe0FKSoqtQ2iQJC/WSV6qdydzc+3atVpPW++Fndlsxt7evkr75s2bCQoKYvLkycD1vWdeXl4UFRXh4eFx2+sbP348zs7O6PV6RowYwY4dO9TK9+rVq2zYsIHWrVszadIklixZctvrAZg9ezazZ89Wh6OjowkNDeX999/ns88+Iz8/n8zMTFq1asUrr7zCc889x7Zt26pd1o3n6BiNRry9vfn7cTvKHKvmr6m6vvelgnlH7WSv1E0kN9ZVl5fKPXaVFEXhj3/8IzNnzmTbtm34+/tTVlZGcHCwOo27uzt9+vTh3//+N7/++ivp6em0atWK2bNns3XrVj755JN669fvZTabSUlJYeDAgfJQ9xtIXqyTvFTvbuSm8qhdbdR7YafT6bh8+bJFW2BgICUlJQwaNEht8/T0RKvVkp+fX6fC7uaq1tnZ2eL9RgaDQd176OTkVOt11Fbr1q25ePEiJpOJDz/8kIiICAwGAwDLly9Xc2HtcLNWq0Wr1VZp3/fqAHn4+A3MZjPJyckce2Ow/LjcRHJjXV3y0qtXLzZu3EiHDh1YtGgRJ06csJinuLgYV1dXPv74Y6ZNm6ael/fPf/4TnU7H1atXb3k6SUPj6Ogo3xcrJC/WSV6qdydzU5fl1PvFE127diUrK4vi4mIAysrKyM3NZdKkSeTk5KjTFRYWYjKZ8PHxqXF5Go2GiorfDqkcO3as1rG4u7vXMfqaPfPMMxw4cEAdPnToEHq9Hq1WS0VFBefOnVPHFRUVAdcP5QghGo5vvvnG4vxZJycnNBoNBoOBQ4cOqe25ubmYTCZatmwp27cQosGo98IuKCgIg8FAREQEOTk5zJ07F51Ox4QJE/j2229Zu3Ytubm5REREEBISol5kUR1PT08yMzMxGo2cP3+epUuX1lNPqurSpQt/+9vfOHDgAAkJCcyePZuIiAgAHnvsMdasWcP777/Phg0bGDNmDEFBQbL3TYgG5sEHH2TNmjWsWbOGM2fOMGfOHAYNGkRwcDBGo5H169cD8M477zBgwADs7e1l+xZCNBj1XtjZ2dmRlJTEuXPnCAgIIDU1leTkZLy9vfnyyy+JjY2lW7duuLi4qD+gNenXrx+DBg2iS5cuDB06lDlz5tRDL6x79dVXCQwMZPDgwURERDBt2jRef/11AKZPn86YMWNYuHAhU6ZMoXnz5mzevNlmsQohrGvTpg2fffYZK1as4KGHHuLatWts3LgRBwcH4uLiePHFF2nVqhWJiYnqebmyfQshGgqNcuOt00WDZTQaad68ORcuXJC9ADeoPF8qODhYzvO4ieTGut+bl6KiIo4dO0bv3r0b1bYo3xfrJC/WSV6qdzdyU1kDXLly5ZankcmzYm+Sn5+v3lz45teYMWNsHZ4QwsY8PDwYOnRooyrqhBCNR71fFdvQtW3blrS0NKvjXFxc6jcYIYQQQog6kMLuJg4ODvj6+to6DCGEEEKIOpNDsUIIIYQQjYQUdkIIIYQQjYQUdkIIIYQQjYQUdkIIAVy+fJnDhw9z6dKlWs/zv//7vxw5coSrV6/exciEEKL2pLC7C+THXoh7y6effoqvry/h4eF4eXnx6aefAnDixAl69OhBixYtmDVrFjfe9nP58uV07NiRsLAwvLy82L9/v63CF0IIlU0Ku4sXLxISEoKrqys9e/YkIyOjVvOlpqY2mCtWs7OzadmyZZX22vzYP/PMM0yfPr0+whRC3MKVK1eYNm0a+/bt47vvviM2NpZZs2ZhMpkYNmwYjz76KEePHiUzM5P4+Hjg+va/ePFiTp48SWZmJpGRkcybN8+2HRFCCGxU2E2YMIHy8nLS09MZNWoUoaGhlJWV2SKU25KTk0NwcHCVQza1+bFPTk4mNTWVhQsX1mfIQohqGI1Gli9fTmBgIACPPPIIFy9eZOfOnVy5coV3332XDh068M4777Bu3ToATCYTa9aswdPT02IeIYSwtXq/j112djY7d+6ksLAQvV7PzJkzWbJkCYcPH6ZPnz71Hc5tGTZsGFOmTGHWrFkW7dZ+7Ldt26aOv3r1KtOmTWPRokXodLrbWnevRV9R5uB627E3Nlp7haU9IeDNf2Eq19g6nAZFcmPdjXnJevspvL29GTduHHD9UUAxMTGMHDmS9PR0evfurd6YPDAwkMzMTAAeeughHnroIeD6dh0bG8vIkSNt0yEhhLhBvRd2hw4don379uj1egDs7e2JjIzE2dmZffv2MWPGDPLy8hgyZAgrV668ZQEUHx9PfHw8qampAJw+fRo/Pz8URcHX15fBgwezdetWwsPDyczM5MiRI+zatYuTJ08SHx/PuHHjmDt3LgCrVq0iNDT0ln344osv0Gg0VQq7W/3YL1iwgNLSUhwcHEhJSaF///7Y2VnfaWoymTCZTOqw0WgEQGunYG8vj/etpLVTLN7FbyQ31t2YF7PZrLanp6fz5JNP4uTkREZGBu+88w4+Pj4W09jb23Pu3DlatGgBwM6dO3nuuedo164dr732msW095rK2O/lPtwNkhfrJC/Vuxu5qcuy6r2wKywspHXr1hZt8+fP58yZMxgMBmJiYujfvz9RUVGEhYWRkJDwu9ZnNBqJjo5m6tSpJCQkUFpayu7du/H09OTEiRNs376dgwcPEhcXR1RUVK0KOz8/P06fPl3t+OTkZMaMGYOPj496KDYvL48VK1bQvXt3cnJyWL58OV5eXiQkJFgt7hYtWsSCBQuqtM/tVoGLS3ntE9BELOxeYesQGizJjXULu1eQnJysDiuKwpw5c/jggw8YMWIEHh4elJeXV5nmiy++UJ8TW15ezquvvsqaNWt49tlnmThxYr33405LSUmxdQgNkuTFOslL9e5kbq5du1braeu9sDObzdjb21dp37x5M0FBQUyePBm4vvfMy8uLoqIiPDw8bnt948ePx9nZGb1ez4gRI9ixY4da+V69epUNGzbQunVrJk2axJIlS257PTcaNGgQn3/+OS+++CKzZ8/mH//4Bxs2bECv1/PVV1/h7OzMyy+/jI+PD3v27GHQoEFVljF79mxeeuklddhoNOLt7c0TTzwhDx+/gdlsJiUlhYEDB+Lo6GjrcBoUyY11t8rLsGHD6NSpE3//+985efIkwcHB6riysjIGDx7M/fffbzF9z549GT16tHo17b1Ivi/WSV6sk7xU727kpvKoXW3Ue2Gn0+m4fPmyRVtgYCAlJSUWBY6npydarZb8/Pw6FXY3V7XOzs4W7zcyGAzq3kMnJ6dar+NWHBwc6Nu3L//85z8JDQ3lH//4BwUFBQwYMECNw83NDX9/f7Kzs60WdlqtFq1WW6Xd0dFRNiIrJC/Vk9xYV5mXb775hi+++ILo6GgAXF1d0Wg0BAQE8MEHH6i5y83NxWQyodfr+eyzzygoKODll18GwMXFBXt7+0aRZ/m+WCd5sU7yUr07mZu6LKfer4rt2rUrWVlZFBcXA9f/B5ybm8ukSZPIyclRpyssLMRkMuHj41Pj8jQaDRUVvx1qOnbsWK1jcXd3r2P0Nfv4449ZtmyZOuzk5KTunfTy8uLXX39Vx1VUVFBQUKBeaCGEsI0HH3yQNWvWsGbNGs6cOcOcOXMYNGgQwcHBGI1G1q9fD8A777zDgAEDsLe3p2PHjrz55pvs2LGD06dPs2DBAp5++mkb90QIIWxQ2AUFBWEwGIiIiCAnJ4e5c+ei0+mYMGEC3377LWvXriU3N5eIiAhCQkLUiyyq4+npSWZmJkajkfPnz7N06dJ66klVNf3YP/300yQlJbFt2zYKCgqYPXs2ZrOZAQMG2CxeIQS0adOGzz77jBUrVvDQQw9x7do1Nm7ciIODA3Fxcbz44ou0atWKxMRE9XSNrl27snr1al566SW6deuGj4+PxX/qhBDCVur9UKydnR1JSUmEh4cTEBBAYGAgycnJeHt78+WXXxIZGcmsWbMYMmQIq1atuuXy+vXrx6BBg+jSpQt6vZ45c+bw7LPP1kNPqrrxx/7y5cv8+c9/Vn/sDQYDW7duZd68efzwww888MADJCYm4uoqty4RwtYGDhzIyZMnq7QPHz6cn376iWPHjtG7d2+L81ufffZZm/3WCCFEdTTKjc/IEQ2W0WikefPmXLhwQS6euIHZbCY5OZng4GA5z+MmkhvrJC/WSV6sk7xYJ3mp3t3ITWUNcOXKlVueRibPir1Jfn4+Op3O6mvMmDG2Dk8IIYQQolr1fii2oWvbti1paWlWx1XegV4IIYQQoiGSwu4mDg4O+Pr62joMIYQQQog6k0OxQgghhBCNhBR2QgghhBCNhBR2QgghhBCNhBR2Qogm7/Llyxw+fJhLly7ZOhQhhPhdbFLYXbx4kZCQEFxdXenZsycZGRm1mi81NbVBXNiwZs0a2rRpg6OjI3379uXnn3+2GJ+dnU3Lli2rnf+ZZ55h+vTpdztMIUQtfPrpp/j6+hIeHo6XlxeffvopACdOnKBHjx60aNGCWbNmcfMtP2+1nQshhC3YpLCbMGEC5eXlpKenM2rUKEJDQykrK7NFKHV24MAB5s2bx6ZNm8jNzUVRFGbOnKmOz8nJITg4uNr/+ScnJ5OamsrChQvrK2QhRDWuXLnCtGnT2LdvH9999x2xsbHMmjULk8nEsGHDePTRRzl69CiZmZnEx8er891qOxdCCFup99udZGdns3PnTgoLC9Hr9cycOZMlS5Zw+PBh+vTpU9/h1NmPP/7I6tWr1We8Tpw4kejoaHX8sGHDmDJlCrNmzaoy79WrV5k2bRqLFi1Cp9Pd1vp7LfqKMgd5DFklrb3C0p4Q8Oa/MJVrbB1OgyK5sa4yL3D9bu7Lly8nMDAQgEceeYSLFy+yc+dOrly5wrvvvouLiwvvvPMOf/3rX5k4cSJQ83YuhBC2VO+F3aFDh2jfvj16vR4Ae3t7IiMjcXZ2Zt++fcyYMYO8vDyGDBnCypUrb1kAxcfHEx8fT2pqKgCnT5/Gz88PRVHw9fVl8ODBbN26lfDwcDIzMzly5Ai7du3i5MmTxMfHM27cOObOnQvAqlWrCA0NrXF9lT/slbKysvD391eHv/jiCzQajdUf/AULFlBaWoqDgwMpKSn0798fOzvrO01NJhMmk0kdNhqNAGjtFOzt5SlwlbR2isW7+I3kxrrKfJjNZjw8PBg9ejRmsxmz2cyyZcsYMWIE//nPf+jVqxeOjo6YzWYMBgOZmZmYzWYAduzYoW7nlW33usp+NJb+3CmSF+skL9W7G7mpy7LqvbArLCykdevWFm3z58/nzJkzGAwGYmJi6N+/P1FRUYSFhZGQkPC71mc0GomOjmbq1KkkJCRQWlrK7t278fT05MSJE2zfvp2DBw8SFxdHVFTULQu7G/3yyy+sXr2aLVu2qG1+fn6cPn26yrR5eXmsWLGC7t27k5OTw/Lly/Hy8iIhIcFqcbdo0SIWLFhQpX1utwpcXMprHWNTsbB7ha1DaLAkN9alpKSon3Nzc3njjTdwcHDgvffe45NPPkGj0ZCcnKxOU15ezieffEKzZs0A+N///V8Ai2kagxvzIn4jebFO8lK9O5mba9eu1Xraei/szGYz9vb2Vdo3b95MUFAQkydPBq7vPfPy8qKoqAgPD4/bXt/48eNxdnZGr9czYsQIduzYoVa+V69eZcOGDbRu3ZpJkyaxZMmSOi37r3/9K0FBQQwZMuSW027YsAG9Xs9XX32Fs7MzL7/8Mj4+PuzZs4dBgwZVmX727Nm89NJL6rDRaMTb25u/H7ejzLFq/poqrZ3Cwu4VzDtqh6lCDjfeSHJjXWVeBg4cqD6gW1EU/vjHPzJz5ky2bduGv78/ZWVlBAcHq/O5u7vTp08fPD09AdT/wN04zb3MbDaTkpJikRcheamO5KV6dyM3lUftaqPeCzudTsfly5ct2gIDAykpKbEocDw9PdFqteTn59epsLu5qnV2drZ4v5HBYFD3Hjo5OdV6HXC9UNu7dy/p6em1mr6goIABAwaocbi5ueHv7092drbVwk6r1aLVaqu073t1APfdd1+dYm3MzGYzycnJHHtjsPy43ERyY11lXhwdHS3y0qtXLzZu3EiHDh1YtGgRJ06csBhfXFyMq6ur2nbze2Nxc17EdZIX6yQv1buTuanLcur9qtiuXbuSlZVFcXExAGVlZeTm5jJp0iRycnLU6QoLCzGZTPj4+NS4PI1GQ0XFb4eajh07VutY3N3d6xj9dUePHmX69Ol89NFH6rmCt+Ll5cWvv/6qDldUVFBQUKD+718IUf+++eYbi/NhnZyc0Gg0GAwGDh06pLbn5uZiMpnk9iZCiAav3gu7oKAgDAYDERER5OTkMHfuXHQ6HRMmTODbb79l7dq15ObmEhERQUhIyC0LJ09PTzIzMzEajZw/f56lS5fe1fjPnTvHsGHDeOWVV+jevTslJSWUlJTccr6nn36apKQktm3bRkFBAbNnz8ZsNqtX1woh6t+DDz7ImjVrWLNmDWfOnGHOnDkMGjSI4OBgjEYj69evB+Cdd95hwIABVk8jEUKIhqTeCzs7OzuSkpI4d+4cAQEBpKamkpycjLe3N19++SWxsbF069YNFxcX9Ue1Jv369WPQoEF06dKFoUOHMmfOnLsa/9atWykqKmLevHm4ubmpr1sxGAxs3bqVt956C39/f5KTk0lMTMTVVW5dIoSttGnThs8++4wVK1bw0EMPce3aNTZu3IiDgwNxcXG8+OKLtGrVisTExDqfgyuEELagUW6+nbpokIxGI82bN+fChQtyjt0NKs+XCg4OlvM8biK5sa4ueSkqKuLYsWP07t270W938n2xTvJineSlencjN5U1wJUrV255Gpk8K/Ym+fn56HQ6q68xY8bYOjwhRD3y8PBg6NChjb6oE0I0HvV+VWxD17ZtW9LS0qyOc3Fxqd9ghBBCCCHqQAq7mzg4OODr62vrMIQQQggh6kwOxQohhBBCNBJS2AkhhBBCNBJS2AkhhBBCNBJS2AkhmqSkpCTat2+Pg4MDXbt25dSpUwCsX7+egIAAdDodY8eO5cKFC+o8NY0TQoiG4J4q7C5evEhISAiurq707NmTjIyMWs2XmpraoC6IeOaZZ5g+fbqtwxCiyfr555+ZPHkyixcv5uzZszz44IOEh4ezZ88eZsyYQUxMDBkZGRiNRkaOHAlQ4zghhGgo7qmrYidMmIBGoyE9PZ1t27YRGhrK999/j4PDvdON5ORkUlNTycrKsnUoQjRZBQUFvP3224wePRqAiIgIhg4dysaNGwkLC2PgwIEAREdH89BDD/HLL7/UOE6eISuEaCjumYooOzubnTt3UlhYiF6vZ+bMmSxZsoTDhw/Tp08fW4dXK1evXmXatGksWrQInU53W8votegryhzkMWSVtPYKS3tCwJv/wlSusXU4DYrkxrofFw6iR48eBAcHq21ZWVn4+/tz4cIFunTporZXPhvW3t6+xnFCCNFQ3DOF3aFDh2jfvj16vR64/mMaGRmJs7Mz+/btY8aMGeTl5TFkyBBWrlx5y8IpPj6e+Ph4UlNTATh9+jR+fn4oioKvry+DBw9m69athIeHk5mZyZEjR9i1axcnT54kPj6ecePGMXfuXABWrVpFaGjoLfuwYMECSktLcXBwICUlhf79+2NnZ/1ouMlkwmQyqcNGoxEArZ2Cvb08Ba6S1k6xeBe/kdxYZzabLd5LS0tZtmwZkZGRnD17ls8//5wZM2ZgZ2fHunXr6N69Oy4uLjz88MPVjqtc1r3s5ryI6yQv1kleqnc3clOXZd0zhV1hYSGtW7e2aJs/fz5nzpzBYDAQExND//79iYqKIiwsjISEhN+1PqPRSHR0NFOnTiUhIYHS0lJ2796Np6cnJ06cYPv27Rw8eJC4uDiioqJuWdjl5eWxYsUKunfvTk5ODsuXL8fLy4uEhASrxd2iRYtYsGBBlfa53SpwcSn/XX1rjBZ2r7B1CA2W5MZSSkqKxfumTZsoKyujTZs2tGjRgsTERDp16oRWqyUrK4vIyEiSk5MJCAiodlxjUpkXYUnyYp3kpXp3MjfXrl2r9bT3TGFnNputHvLYvHkzQUFBTJ48Gbi+98zLy4uioiI8PDxue33jx4/H2dkZvV7PiBEj2LFjh1oxX716lQ0bNtC6dWsmTZrEkiVLbrm8DRs2oNfr+eqrr3B2dubll1/Gx8eHPXv2MGjQoCrTz549m5deekkdNhqNeHt78/fjdpQ5yqGfSlo7hYXdK5h31A5ThRxuvJHkxrrjr/cjJSWFgQMHcuDAAXbv3s3+/fvp3LkzAKNHjyY7O5uYmBgAFi9erP721DTuXmc2m9W8yEPdfyN5sU7yUr27kZvKo3a1cc8UdjqdjsuXL1u0BQYGUlJSYlEYeXp6otVqyc/Pr1Nhd3M17OzsbPF+I4PBoO49dHJyqtXyCwoKGDBggLo8Nzc3/P39yc7OtlrYabVatFptlfZ9rw6QB5LfwGw2k5yczLE3BsuPy00kN9ZV/getoKCA559/ntjYWB5++GGLaXx8fEhISGDNmjVVfgNqGtcYODo6yvfFCsmLdZKX6t3J3NRlOffM7U66du1KVlYWxcXFAJSVlZGbm8ukSZPIyclRpyssLMRkMuHj41Pj8jQaDRUVvx2iOnbsWK1jcXd3r2P04OXlxa+//qoOV1RUUFBQgKenZ52XJYT4fUwmEyEhIYwYMYKRI0dSUlJCSUkJinL9fMT33nuPTp06ERISUmXemsYJIYSt3TOFXVBQEAaDgYiICHJycpg7dy46nY4JEybw7bffsnbtWnJzc4mIiCAkJES9yKI6np6eZGZmYjQaOX/+PEuXLr2r8T/99NMkJSWxbds2CgoKmD17NmazmQEDBtzV9QohqkpLS+PUqVOsXbsWNzc39ZWXl8elS5dYunQpy5YtqzJfTeOEEKIhuGcKOzs7O5KSkjh37hwBAQGkpqaSnJyMt7c3X375JbGxsXTr1g0XFxfWr19/y+X169ePQYMG0aVLF4YOHcqcOXPuavwGg4GtW7fy1ltv4e/vT3JyMomJibi6yq1LhKhvvXr1orS0FEVRLF6+vr60aNGCixcv0qNHjyrz1TROCCEagnvmHDuAdu3asXv37irtffv2JS0trdr5Hn/8cU6fPm3RZmdnx5YtWyzaxo4dC2AxbeXn+Ph4tS0sLEz97Ovrqx6+uZXhw4czfPjwWk0rhBBCCFFX98weu4YuPz8fnU5n9TVmzBhbhyeEEEKIJuCe2mPXkLVt27bavYYuLi71G4wQQgghmiQp7O4QBwcHfH19bR2GEEIIIZowORQrhBBCCNFISGEnhBBCCNFISGEnhBBCCNFISGEnhBBCCNFISGEnhGiSkpKSaN++PQ4ODnTt2pVTp04BsH79egICAtDpdIwdO5YLFy6o88TFxeHt7Y2LiwuPP/64xeMMhRCiIbBJYXfx4kVCQkJwdXWlZ8+eZGRk1Gq+1NTUBnPlaXZ2Ni1btqx2/DPPPMP06dMt2r755hsMBgOtWrXi3XffvdshCiGq8fPPPzN58mQWL17M2bNnefDBBwkPD2fPnj3MmDGDmJgYMjIyMBqNjBw5EoCffvqJt956i8TERL7//ns6dOhgcbNyIYRoCGxS2E2YMIHy8nLS09MZNWoUoaGhlJWV2SKU25KTk0NwcDCXLl2yOj45OZnU1FQWLlyotp0/f57hw4czduxYDh06xIcffsjevXvrK2QhxA0KCgp4++23GT16NHq9noiICI4fP87GjRsJCwtj4MCBtGvXjujoaA4cOMAvv/zC8ePH6d27N4888gjt2rVj0qRJZGdn27orQghhod7vY5ednc3OnTspLCxEr9czc+ZMlixZwuHDh+nTp099h3Nbhg0bxpQpU5g1a1aVcVevXmXatGksWrQInU6ntn/44Ye0bduWefPmodFoeOONN1i3bh1PPPFEndbda9FXlDnI82Urae0VlvaEgDf/halcY+twGhTJTVWnFw8FoEePHgQHB6vtWVlZ+Pv7c+HCBbp06aK229vbq++dO3fm66+/Ji0tDT8/P1auXMnAgQPrtwNCCHEL9V7YHTp0iPbt26PX64HrP5iRkZE4Ozuzb98+ZsyYQV5eHkOGDGHlypUWxZE18fHxxMfHk5qaClx/tqufn5/6QO/BgwezdetWwsPDyczM5MiRI+zatYuTJ08SHx/PuHHjmDt3LgCrVq0iNDT0ln344osv0Gg0Vgu7BQsWUFpaioODAykpKfTv3x87OzvS09N54okn0Giu/4Ht2bMnr732WrXrMJlMmEwmddhoNAKgtVOwt6/ds2mbAq2dYvEufiO5qcpsNmM2m9XPAKWlpSxbtozIyEjOnj3L559/zowZM7Czs2PdunV0794dFxcX/P39CQ0NpVu3bgD4+flx4MABdTn3upvzIq6TvFgneane3chNXZZV74VdYWEhrVu3tmibP38+Z86cwWAwEBMTQ//+/YmKiiIsLIyEhITftT6j0Uh0dDRTp04lISGB0tJSdu/ejaenJydOnGD79u0cPHiQuLg4oqKialXY+fn5cfr06SrteXl5rFixgu7du5OTk8Py5cvx8vIiISEBo9FI586d1Wnd3d0pLCysdh2LFi1iwYIFVdrndqvAxaW8dp1vQhZ2r7B1CA2W5OY3ycnJ6ueUlBQANm3aRFlZGW3atKFFixYkJibSqVMntFotWVlZREZGkpyczA8//MC2bdtYunQpnp6e7Nixg759+xIdHa3+h60xqMyLsCR5sU7yUr07mZtr167Vetp6L+zMZrN6eONGmzdvJigoiMmTJwPX9555eXlRVFSEh4fHba9v/PjxODs7o9frGTFiBDt27FAr36tXr7JhwwZat27NpEmTWLJkyW2vB2DDhg3o9Xq++uornJ2defnll/Hx8WHPnj04ODig1WrVaZ2dnWv8h5o9ezYvvfSSOmw0GvH29uaJJ57gvvvu+11xNiZms5mUlBQGDhyIo6OjrcNpUCQ31t2YlwMHDrB7927279+v/sdr9OjRZGdnExMTA8DixYuxt7fn66+/Zvz48URFRQHw5z//GQ8PDzw9PenatauNenPnyPfFOsmLdZKX6t2N3FQetauNei/sdDodly9ftmgLDAykpKSEQYMGqW2enp5otVry8/PrVNjdXCw5OztbvN/IYDCoew+dnJxqvY7qFBQUMGDAAHVdbm5u+Pv7q1fQnj9/Xp22uLi4xnVqtVqLQrCSo6OjbERWSF6qJ7mxrqCggOeff57Y2Fgefvhhi3E+Pj4kJCSwZs0ai9+OCxcuqLk0Go1cu3YNOzu7RpVf+b5YJ3mxTvJSvTuZm7osp94Lu65du5KVlUVxcTFubm6UlZWRm5vLq6++yr59+9TpCgsLMZlM+Pj41Lg8jUZDRcVvh5qOHTtW61jc3d3r3oEaeHl5qffCAqioqKCgoABPT0+cnZ3ZsmWLOu748eN4enre0fULIWrHZDIREhLCiBEjGDlyJCUlJQC4urqi0Wh477336NSpEyEhIeo8jz32GBMmTOCRRx5Br9cTFxeHh4cHgYGBNuqFEEJUVe+3OwkKCsJgMBAREUFOTg5z585Fp9MxYcIEvv32W9auXUtubi4RERGEhISoF1lUx9PTk8zMTIxGI+fPn2fp0qX11JOqnn76aZKSkti2bRsFBQXMnj0bs9nMgAEDGD58OAcPHmTPnj2YzWaWLl3Kk08+abNYhWjK0tLSOHXqFGvXrsXNzU195eXlcenSJZYuXcqyZcss5hk1ahSvvfYay5cvJywsjMuXL7Njxw7ZWyGEaFDqvbCzs7MjKSmJc+fOERAQQGpqKsnJyXh7e/Pll18SGxtLt27dcHFxYf369bdcXr9+/Rg0aBBdunRh6NChzJkzpx56YZ3BYGDr1q289dZb+Pv7k5ycTGJiIq6urrRq1YqYmBiCg4PR6/VkZWWpV+MKIepXr169KC0tRVEUi5evry8tWrTg4sWL9OjRw2IejUbDvHnzyMvLo7S0lP/85z/qFbJCCNFQ1PuhWIB27dqxe/fuKu19+/YlLS2t2vkef/zxKlej2tnZWRziBBg7diyAxbSVn+Pj49W2G+8a7+vri6LU/rYQ1U0/fPhwhg8fbnWeF154gSeffJLvv/+exx57jGbNmtV6fUIIIYQQt2KTwq4hy8/Pr/acmcGDB/PRRx/9ruX7+fnh5+f3u5YhhBBCCGGNFHY3adu2bbV7DV1cXOo3GCGEEEKIOpDC7iYODg74+vraOgwhhBBCiDqr94snhBBCCCHE3SGFnRBCCCFEIyGFnRBCCCFEIyGFnRCiSUpKSqJ9+/Y4ODjQtWtX9akx69evJyAgAJ1Ox9ixY7lw4YI6T1xcHN7e3ri4uPD444+Tk5Njq/CFEMKqBlfYXbx4kZCQEFxdXenZsycZGRm1mi81NbVBXPSwZs0a2rRpg6OjI3379uXnn39Wxy1cuBC9Xk+zZs0YPny4xR8MIUT9+fnnn5k8eTKLFy/m7NmzPPjgg4SHh7Nnzx5mzJhBTEwMGRkZGI1GRo4cCcBPP/3EW2+9RWJiIt9//z0dOnSwuBemEEI0BA2usJswYQLl5eWkp6czatQoQkNDKSsrs3VYtXLgwAHmzZvHpk2byM3NRVEUZs6cCcC+ffv4+OOP2bdvH2lpaZSXl/PSSy/ZOGIhmqaCggLefvttRo8ejV6vJyIiguPHj7Nx40bCwsIYOHAg7dq1Izo6mgMHDvDLL79w/PhxevfuzSOPPEK7du2YNGkS2dnZtu6KEEJYaFC3O8nOzmbnzp0UFhai1+uZOXMmS5Ys4fDhw/Tp08fW4d3Sjz/+yOrVqxkwYAAAEydOJDo6GoAjR44QHBxMx44dAXj22WeJjY2t8zp6LfqKMgfXOxf0PU5rr7C0JwS8+S9M5Rpbh9OgSG6qOr14KAA9evQgODhYbc/KysLf358LFy7QpUsXtd3e3l5979y5M19//TVpaWn4+fmxcuVKBg4cWL8dEEKIW2hQhd2hQ4do3749er0euP5jGhkZibOzM/v27WPGjBnk5eUxZMgQVq5ciU6nq3F58fHxxMfHk5qaClx/rJifn5/6TMjBgwezdetWwsPDyczM5MiRI+zatYuTJ08SHx/PuHHj1Oe5rlq1itDQ0BrXN3HiRIvhyj8WAA899BCrV6/mhRdeoFmzZqxbt67GPwomkwmTyaQOG41GALR2Cvb2tX/0WWOntVMs3sVvJDdVmc1mzGaz+hmgtLSUZcuWERkZydmzZ/n888+ZMWMGdnZ2rFu3ju7du+Pi4oK/vz+hoaHq82H9/Pw4cOCAupx73c15EddJXqyTvFTvbuSmLstqUIVdYWEhrVu3tmibP38+Z86cwWAwEBMTQ//+/YmKiiIsLIyEhITftT6j0Uh0dDRTp04lISGB0tJSdu/ejaenJydOnGD79u0cPHiQuLg4oqKiblnY3eiXX35h9erV6nNshwwZQocOHejQoQNwfY/Ba6+9Vu38ixYtYsGCBVXa53arwMWlvI49bfwWdq+wdQgNluTmN8nJyernlJQUADZt2kRZWRlt2rShRYsWJCYm0qlTJ7RaLVlZWURGRpKcnMwPP/zAtm3bWLp0KZ6enuzYsYO+ffsSHR2NRtN49ohW5kVYkrxYJ3mp3p3MzbVr12o9bYMq7Mxms3ro40abN28mKCiIyZMnA9f3nnl5eVFUVISHh8dtr2/8+PE4Ozuj1+sZMWIEO3bsUKviq1evsmHDBlq3bs2kSZNYsmRJnZb917/+laCgIIYMGQLAZ599Rn5+PpmZmbRq1YpXXnmF5557jm3btlmdf/bs2Rbn4BmNRry9vfn7cTvKHKvmqKnS2iks7F7BvKN2mCoazx/XO0FyU9WJN5/EbDaTkpLCwIEDOXDgALt372b//v107twZgNGjR5OdnU1MTAwAixcvxt7enq+//prx48cTFRUFwJ///Gc8PDzw9PSka9euNurRnXNjXhwdHW0dToMhebFO8lK9u5GbyqN2tdGgCjudTsfly5ct2gIDAykpKWHQoEFqm6enJ1qtlvz8/DoVdjdXvM7OzhbvNzIYDOreQycnp1qvA2DDhg3s3buX9PR0te3DDz8kIiICg8EAwPLly9X+WjukrNVq0Wq1Vdr3vTqA++67r07xNGZms5nk5GSOvTFYflxuIrmpWUFBAc8//zyxsbE8/PDDFuN8fHxISEhgzZo1Fr8PFy5cUHNpNBq5du0adnZ2jSq/jo6Ojao/d4rkxTrJS/XuZG7qspwGVdh17dqVrKwsiouLcXNzo6ysjNzcXF599VX27dunTldYWIjJZMLHx6fG5Wk0GioqfjsMdezYsVrH4u7uXvcOAEePHmX69OkkJSWp5woCVFRUcO7cOXW4qKgIgPJyOawqRH0zmUyEhIQwYsQIRo4cSUlJCQCurq5oNBree+89OnXqREhIiDrPY489xoQJE3jkkUfQ6/XExcXh4eFBYGCgjXohhBBVNajCLigoCIPBQEREBG+99RZr1qxBp9MxYcIEFi9ezNq1axkwYABRUVGEhIRYFE7WeHp6kpmZidFoxGQysXTp0rsa/7lz5xg2bBivvPIK3bt3V/9YNGvWjMcee4zo6Gg8PT35wx/+wPLlywkKCpK9b0LYQFpaGqdOneLUqVOsXbtWbc/NzaV58+YsXbqUXbt2WcwzatQoTp06xfLly/n5558JCAhgx44dsrdCCNGgNKj72NnZ2ZGUlMS5c+cICAggNTWV5ORkvL29+fLLL4mNjaVbt264uLiwfv36Wy6vX79+DBo0iC5dujB06FDmzJlzV+PfunUrRUVFzJs3Dzc3N/UFMH36dMaMGcPChQuZMmUKzZs3Z/PmzXc1HiGEdb169aK0tBRFUSxevr6+tGjRgosXL9KjRw+LeTQaDfPmzSMvL4/S0lL+85//qFfICiFEQ9Gg9tgBtGvXjt27d1dp79u3L2lpadXO9/jjj3P69GmLNjs7O/Wq1Epjx44FsJi28nN8fLzaduMd5X19fVGUW98yIjIyksjISKvjtFotK1asYMWKFbdcjhBCCCHE7WhQe+wauvz8fHQ6ndXXmDFjbB2eEEIIIZq4BrfHriFr27ZttXsNXVxc6jcYIYQQQoibSGFXBw4ODvj6+to6DCGEEEIIq+RQrBBCCCFEIyGFnRBCCCFEIyGFnRBCCCFEIyGFnRCi0UtMTKR9+/Y4ODjQvXt3zpw5A8D69esJCAhAp9MxduxYLly4YHWerl27curUKVuFL4QQtWaTwu7ixYuEhITg6upKz549ycjIqNV8qampDebihezsbFq2bFnncQDPPPMM06dPv1uhCSFu8NNPPzFx4kQWL17M2bNn8ff3JzY2lq+++ooZM2YQExNDRkYGRqORkSNHWp3nwQcfJDw83MY9EUKIW7PJVbETJkxAo9GQnp7Otm3bCA0N5fvvv8fB4d64SDcnJ4fg4GAuXbpUp3EAycnJpKamkpWVdbfDFEIAp06dYvHixYwePRqAqVOn8tRTT7F582bCwsIYOHAgANHR0Tz00EP88ssvVeaJiIhg6NChNuuDEELUVr1XUtnZ2ezcuZPCwkL0ej0zZ85kyZIlHD58mD59+tR3OLdl2LBhTJkyhVmzZtVp3NWrV5k2bRqLFi1Cp9Pd1rp7LfqKMgfX25q3MdLaKyztCQFv/gtTucbW4TQokhs4vXgoTz31lEXbDz/8QNu2bbl48SIPP/yw2m5vb6++3zxPVlYW/v7+dz9gIYT4neq9sDt06BDt27dHr9cD139EIyMjcXZ2Zt++fcyYMYO8vDyGDBnCypUrb1kAxcfHEx8fT2pqKnD98WB+fn7qcx8HDx7M1q1bCQ8PJzMzkyNHjrBr1y5OnjxJfHw848aNY+7cuQCsWrWK0NDQW/bhiy++QKPRWC3eahq3YMECSktLcXBwICUlhf79+2NnZ/1ouMlkwmQyqcNGoxEArZ2Cvf2tH2/WVGjtFIt38RvJDZjNZovh0tJSYmJiePLJJ2nevDmff/45M2bMwM7OjnXr1tG9e3dcXFws5istLWXZsmVERkZWWV5jUtm3xtzH2yF5sU7yUr27kZu6LKveC7vCwkJat25t0TZ//nzOnDmDwWAgJiaG/v37ExUVRVhYGAkJCb9rfUajkejoaKZOnUpCQgKlpaXs3r0bT09PTpw4wfbt2zl48CBxcXFERUXVqrDz8/Or8lzaW43Ly8tjxYoVdO/enZycHJYvX46XlxcJCQlWi7tFixaxYMGCKu1zu1Xg4lJ+yxibmoXdK2wdQoPVlHOTnJxsMbxp0ybKy8sZOHAg//3vf/n888/p1KkTWq2WrKwsIiMjrc5TVlZGmzZtqoxrjFJSUmwdQoMkebFO8lK9O5mba9eu1Xraei/szGazesjjRps3byYoKIjJkycD1/eeeXl5UVRUhIeHx22vb/z48Tg7O6PX6xkxYgQ7duxQK9+rV6+yYcMGWrduzaRJk1iyZMltr+dWNmzYgF6v56uvvsLZ2ZmXX34ZHx8f9uzZw6BBg6pMP3v2bF566SV12Gg04u3tzd+P21HmWDV/TZXWTmFh9wrmHbXDVNE0DzdWR3IDJ958Uv28d+9edu/ezd69eykoKGDkyJGMHj2a7OxsYmJiAFi8eLHF71PlPPv376dz5871Hn99MpvNpKSkMHDgQBwdHW0dToMhebFO8lK9u5GbyqN2tVHvhZ1Op+Py5csWbYGBgZSUlFgUOJ6enmi1WvLz8+tU2N1c1To7O1u838hgMKh7D52cnGq9jttRUFDAgAED1Djc3Nzw9/cnOzvbamGn1WrRarVV2ve9OoD77rvvrsZ6LzGbzSQnJ3PsjcHy43ITyc1vcnNzef7554mNjSUwMJCCggIcHR1xdHTEx8eHhIQE1qxZY/E7ceM8N56L19hV5kVYkrxYJ3mp3p3MTV2WU++3O+natStZWVkUFxcDUFZWRm5uLpMmTSInJ0edrrCwEJPJhI+PT43L02g0VFT8dqjp2LFjtY7F3d29jtHfPi8vL3799Vd1uKKigoKCAjw9PestBiGaol9//ZWnnnqKESNGMHLkSEpKSvj1119RlOvnHr733nt06tSJkJCQGucpKSlR5xFCiIaq3gu7oKAgDAYDERER5OTkMHfuXHQ6HRMmTODbb79l7dq15ObmEhERQUhIiHqRRXU8PT3JzMzEaDRy/vx5li5dWk89qZunn36apKQktm3bRkFBAbNnz8ZsNjNgwABbhyZEo7Z7924yMzNZu3Ytbm5utGzZkrFjx5KXl8elS5dYunQpy5Ytq3GeyldeXp6NeiGEELVT74WdnZ0dSUlJnDt3joCAAFJTU0lOTsbb25svv/yS2NhYunXrhouLC+vXr7/l8vr168egQYPo0qULQ4cOZc6cOfXQi7ozGAxs3bqVt956C39/f5KTk0lMTMTVVW5dIsTdNGLECBRFUV+lpaUkJCTg6+tLixYtuHjxIj169KhxnspXQ7lBuhBCVMcmdwRu164du3fvrtLet29f0tLSqp3v8ccfr3LFqZ2dHVu2bLFoGzt2LIDFtJWf4+Pj1bawsDD1s6+vb50Os9Q0fXXjhg8fzvDhw2u9DiGEEEKIupBnxd4kPz8fnU5n9TVmzBhbhyeEEEIIUa174xle9aht27bV7jV0cXGp32CEEEIIIepACrubODg4yHk0QgghhLgnyaFYIYQQQohGQgo7IYQQQohGQgo7IYQQQohGQgo7IYQQQohGQgo7IUSjlpiYSPv27XFwcKBr166cOnUKgA0bNhAQEIBOp2Ps2LFcuHBBnefEiRP06NGDFi1aMGvWLHmUmBDinmGTwu7ixYuEhITg6upKz549ycjIqNV8qampDeKK1TVr1tCmTRscHR3p27cvP//8szrum2++wWAw0KpVK959990q83777bd07NixPsMVosn66aefmDhxIosXL+bs2bM8+OCDvPDCC6Snp/O3v/2NmJgYMjIyMBqNjBw5EgCTycSwYcN49NFHOXr0KJmZmRY3NhdCiIbMJoXdhAkTKC8vJz09nVGjRhEaGkpZWZktQqmzAwcOMG/ePDZt2kRubi6KojBz5kwAzp8/z/Dhwxk7diyHDh3iww8/ZO/eveq8x44dY+TIkZhMJluFL0STcurUKRYvXszo0aPR6/VERESQlpbG3r17GT9+PAMHDqRdu3ZER0dz4MABfvnlF3bu3MmVK1d499136dChA++88w7r1q2zdVeEEKJW6v0+dtnZ2ezcuZPCwkL0ej0zZ85kyZIlHD58mD59+tR3OHX2448/snr1agYMGADAxIkTiY6OBuDDDz+kbdu2zJs3D41GwxtvvMG6det44oknuHr1KqGhobz44ou1+iNhMpksCkCj0QjAn5bsocxRni9bSWunsLA7PPrWLkwVGluH06A09dycePNJnnzySQDMZjMAmZmZdOjQAaPRSNu2bdX2iooK9f0///kPvXr1wtHREbPZjMFgIDMzU522sarsX2PvZ11JXqyTvFTvbuSmLsuq98Lu0KFDtG/fHr1eD4C9vT2RkZE4Ozuzb98+ZsyYQV5eHkOGDGHlypXodLoalxcfH098fDypqanA9WfC+vn5qQ/sHjx4MFu3biU8PJzMzEyOHDnCrl27OHnyJPHx8YwbN465c+cCsGrVKkJDQ2tc38SJEy2Gs7Ky8Pf3ByA9PZ0nnngCjeb6H9GePXvy2muvAeDo6Mi3337Ljz/+WKvCbtGiRSxYsKBK+9xuFbi4lN9y/qZmYfcKW4fQYDXV3CQnJ1sMm81m3n77bYYPH84vv/zC1q1b6dy5M3Z2dmzatAl/f38OHjxIeno6Go3GYv7y8nI++eQTmjVrVt/dqHcpKSm2DqFBkrxYJ3mp3p3MzbVr12o9bb0XdoWFhbRu3dqibf78+Zw5cwaDwUBMTAz9+/cnKiqKsLAwEhISftf6jEYj0dHRTJ06lYSEBEpLS9m9ezeenp6cOHGC7du3c/DgQeLi4oiKirplYXejX375hdWrV7NlyxZ1XZ07d1bHu7u7U1hYCICTkxOenp78+OOPtVr27Nmzeemllyz64e3tzd+P21HmaF/rGBu763ulKph31K5J7pWqSVPPzYk3n7QYfv3117n//vtZsmQJX3zxBWfPnuXNN9/kD3/4A4cPH+aDDz4gODiY/fv3U1ZWRnBwsDqvu7s7ffr0wdPTs767UW/MZjMpKSkMHDgQR0dHW4fTYEherJO8VO9u5KbyqF1t1HthZzabsbevWphs3ryZoKAgJk+eDFzfe+bl5UVRUREeHh63vb7x48fj7OyMXq9nxIgR7NixQ92lefXqVTZs2EDr1q2ZNGkSS5YsqdOy//rXvxIUFMSQIUOA648j02q16nhnZ+c6Vdk30mq1FsuqtO/VAdx33323tczGyGw2k5yczLE3BsuPy00kN7/5+uuvef/99/n3v/+Ni4sLzZo1IzU1lby8PP7xj39w5coVxo8fj729Pffffz8nTpywyFlxcTGurq5NIo+Ojo5Nop91JXmxTvJSvTuZm7osp94LO51Ox+XLly3aAgMDKSkpYdCgQWqbp6cnWq2W/Pz8OhV2NxdSzs7OFu83MhgM6t5DJyenWq8Drt8qYe/evaSnp6ttLVu25Pz58+pwcXFxnZcrhLizcnNzGTt2LLGxsXTu3NniXJW2bduyfft21qxZo/6Hs0ePHqxdu9ZifpPJRMuWLes9diGEqKt6vyq2a9euZGVlUVxcDEBZWRm5ublMmjSJnJwcdbrCwkJMJhM+Pj41Lk+j0agnPsP1K09ry93dvY7RX3f06FGmT5/ORx99pJ4rCNf/IBw6dEgdPn78eKM+dCNEQ/frr7/y1FNPMWLECEaOHElJSQklJSXqfenee+89OnXqREhIiDrPn/70J4xGI+vXrwfgnXfeYcCAAVaPNAghRENT74VdUFAQBoOBiIgIcnJymDt3LjqdjgkTJvDtt9+ydu1acnNziYiIICQkxKJwssbT05PMzEyMRiPnz59n6dKldzX+c+fOMWzYMF555RW6d++u/qEAGD58OAcPHmTPnj2YzWaWLl2qXpUnhKh/u3fvJjMzk7Vr1+Lm5oabmxstW7bk3LlzXLp0iaVLl7Js2TKLeRwcHIiLi+PFF1+kVatWJCYm1vk0DSGEsJV6L+zs7OxISkri3LlzBAQEkJqaSnJyMt7e3nz55ZfExsbSrVs3XFxc1P8x16Rfv34MGjSILl26MHToUObMmXNX49+6dStFRUXMmzdP/UPh5uYGQKtWrYiJiSE4OBi9Xk9WVpZ6xa0Qov6NGDECRVEsXqWlpej1elq0aMHFixfp0aNHlfmGDx/OTz/9xIYNGzh16pTFRVFCCNGQaRR5Vs4dl5uby/fff89jjz12x26PYDQaad68ORcuXJCLJ25QeYFAcHCwnMB7E8mNdZIX6yQv1klerJO8VO9u5KayBrhy5cotTyOr94snGrr8/HwCAwOtjhs8eDAfffTRLZfh5+eHn5/fnQ5NCCGEEKJGUtjdpG3btqSlpVkd5+LiUr/BCCGEEELUgRR2N3FwcMDX19fWYQghhBBC1Fm9XzwhhBBCCCHuDinshBBCCCEaCSnshBBCCCEaCSnshBBCCCEaCSnshBBCCCEaCSnshBBCCCEaCSnshBBCCCEaCbmP3T2i8slvxcXF8viWG5jNZq5du4bRaJS83ERyY53kxTrJi3WSF+skL9W7G7kxGo3Ab7VATaSwu0dcvHgRQB5VJoQQQjRRxcXFNG/evMZppLC7R7Rs2RK4/izbW/2jNiVGoxFvb2/OnDlzywcjNzWSG+skL9ZJXqyTvFgneane3ciNoigUFxfTtm3bW04rhd09ws7u+umQzZs3l43ICnd3d8lLNSQ31klerJO8WCd5sU7yUr07nZva7tSRiyeEEEIIIRoJKeyEEEIIIRoJKezuEVqtlvnz56PVam0dSoMieame5MY6yYt1khfrJC/WSV6qZ+vcaJTaXDsrhBBCCCEaPNljJ4QQQgjRSEhhJ4QQQgjRSEhhJ4QQQgjRSEhhJ4RodC5fvszhw4e5dOmSrUMRQoh6JYXdPeDEiRP06NGDFi1aMGvWrFo9K64xuXDhAn5+fpw+fVptqykn33zzDQaDgVatWvHuu+/aIOL6kZiYSPv27XFwcKBr166cOnUKkNx8+umn+Pr6Eh4ejpeXF59++ikgebnR4MGDiY+PB2ru+2effYaPjw9t27Zl69atNoj07psxYwYajUZ9PfDAA4B8X2706quvMmzYMHW4KecmPj7e4vtS+YqPj28425IiGrT//ve/iq+vrzJ16lQlOztbCQ4OVj744ANbh1Vvzp8/r/Tq1UsBlNzcXEVRas7JuXPnFHd3d2XBggXKDz/8oDzyyCPK119/bcMe3B3Z2dlKixYtlI8//lgpKipSnn76aSUoKKjJ5+by5ctKq1atlPT0dEVRFGX9+vWKj49Pk8/LjTZv3qwAyvr162vs+3fffac4OTkpa9euVTIyMpQHHnhA+f77720c/Z33xz/+Ufnyyy+VS5cuKZcuXVKMRqN8X26Qnp6uNGvWTPnpp58URZHfX5PJpH5XLl26pJw5c0Zp1aqV8u9//7vBbEtS2DVwO3bsUFq0aKFcvXpVURRFSUtLU/r06WPjqOpP//79lRUrVlgUdjXlJCYmRunUqZNSUVGhKIqiJCQkKOPGjbNJ7HfT559/rqxevVod/vrrr5U//OEPTT43+fn5yubNm9Xhyj9KTT0vlS5evKjo9XqlY8eOyvr162vse2RkpPLkk0+q8y5fvlx5/fXXbRL33WI2mxV3d3eluLjYol2+L9eVl5crvXr1UubNm6e2SW4svf3228rkyZMb1LYkh2IbuPT0dHr37o2LiwsAgYGBZGZm2jiq+rN27VpmzJhh0VZTTtLT03niiSfQaDQA9OzZk2PHjtVv0PXgqaeeYsqUKepwVlYW/v7+TT433t7ejBs3DgCz2UxMTAwjR45s8nmp9PLLLzNy5Eh69+4N1Nz39PR0+vXrp87bGPPy3XffUVFRQdeuXfnDH/7A4MGDyc/Pl+/L/3n//ff57rvv8PX1JSkpidLSUsnNDf773/+yYsUK5syZ06C2JSnsGjij0Yifn586rNFosLe3bzInhd/Y90o15eTmce7u7hQWFtZLrLZSWlrKsmXLeOGFFyQ3/yc9PR0PDw927drFP//5T8kLsHfvXr766iuWLl2qttXU96aQl8zMTDp27MimTZvIyMjAwcGBKVOmyPcFKCkpYf78+bRv3568vDxiYmL4n//5H8nNDbZs2UKvXr3w9fVtUNuSFHYNnIODQ5XHkjg7O3Pt2jUbRWR7NeXk5nFNIVfz58/H1dWV8PBwyc3/CQwMZPfu3fj7+0teuL5nYerUqaxatQo3Nze1vaa+N4W8jBs3jqNHj/LHP/4Rf39/Vq5cSUpKChUVFU36+wKwfft2rl69yt69e1mwYAEpKSkUFxfzwQcfNPncVHr//fd54YUXgIa1LUlh18C1bNmS8+fPW7QVFxfj5ORko4hsr6ac3Dyusefq66+/JjY2li1btuDo6Ci5+T8ajYZHH32UDRs2sH379iafl4ULF9KjRw+GDh1q0V5T35tCXm7WunVrKioq8PDwaNLfF4CCggJ69+5Nq1atgOvFSWBgIJcvX27yuQHIzs4mOzubgQMHAg1rW5LCroHr0aMHhw4dUodzc3MxmUy0bNnShlHZVk05uXnc8ePH8fT0tEWYd11ubi5jx44lNjaWzp07A5Kbb775hlmzZqnDTk5OaDQaDAZDk87Lli1bSExMRKfTodPp2LJlC9OmTWPDhg3V9r0p5GXWrFls2bJFHT506BB2dnZ06dKlSX9fALy8vPj1118t2vLy8li+fHmTzw3AJ598wlNPPYWjoyNQ8/ZS73m5a5dliDvCbDYr999/v3o5eXh4uPLUU0/ZOKr6xw1XxdaUk/PnzyvOzs5KSkqKUlpaqgwePFh58cUXbRX2XXPt2jWlc+fOyuTJk5Xi4mL1VVpa2qRzU1hYqLi7uyurV69W8vPzlfHjxyuDBw9u8t+ZM2fOKLm5uepr1KhRSnR0dI19T0tLU1xdXZWMjAyluLhY6dq1q/KPf/zDxj25szZt2qT4+fkpe/bsUf71r38pDz74oBIWFtbkvy+KoigXLlxQ3N3dlVWrVilnzpxRVqxYoTg7Oyv5+flNPjeKoiiPPfaYsm7dOnW4IW1LUtjdAxITExUXFxflvvvuU+6//37l5MmTtg6p3t1Y2ClKzTlZtWqV4ujoqLRo0ULx8/NTioqKbBDx3ZWQkKAAVV65ublNPje7d+9WOnfurLi5uSl//vOflXPnzimKIt+ZG02YMEFZv369oig1933OnDmKk5OT4u7urjz66KPKtWvXbBTx3fPaa68pzZs3V1q2bKnMmDFDKSkpURRFvi+KoigHDhxQevfurfzhD39Q2rdvryQlJSmKIrm5du2a4uTkpJw6dcqivaFsSxpFaWKPMbhHFRUVcezYMXr37s19991n63AahJpykpuby/fff89jjz1Gs2bNbBSh7UhurJO8WFdT3zMzMzl79ix9+/ZttOdLVUe+L9WT3FjXELYlKeyEEEIIIRoJuXhCCCGEEKKRkMJOCCGEEKKRkMJOCCGEEKKRkMJOCCGEEKKRkMJOCCGEEKKRkMJOCHFPiY+PR6PRVHnt2bPH1qHdUW+++SZhYWG2DkMIcY9xsHUAQghRVwEBAezfv9+i7U7cL0uj0ZCbm4uvr+/vXtbv9dprr1FRUWHrMDh9+jR+fn7InbGEuDdIYSeEuOfY29uj0+lsHcZd5ezsbOsQhBD3IDkUK4RoVHbt2kWXLl3Q6XSEh4djMpnUcWvXrqVdu3a4ubkRGhpKSUkJAJ06dUKj0QDg5+eHRqPho48+AqoeEk1NTbXYo+fr68uePXuYPXs2Hh4enDx5Uh23ceNG/P39adWqFXPmzKnTXq+b13v69Gk0Gg1/+9vfaNasGe+99x4dOnSgc+fOlJSU8PjjjzN58mQ6depE69atefPNNy2WFxsbi6+vL23atOHNN9+02Buo0Wg4efIkU6dOpWXLlly9ehW4Xlz6+fmp02g0Gv7973+r8y1cuBC9Xk+LFi0IDw+nvLwcgLCwMObNm8df//pXmjVrRkBAAFlZWep8y5cvp127dtx3331ERERgNpsBUBSF6OhofHx8aNOmDStWrKh1voQQ/+euPaxMCCHugvXr1yt2dnZK8+bN1VdGRoaiKIry448/Kk5OTsratWuVH374QenSpYuycOFCRVEU5cSJE4q9vb3yr3/9S8nPz1f+9Kc/KUuWLFEURVGMRqNy6dIlBVDS09OVS5cuKaWlpYqiKMr8+fOVCRMmqOvfu3ev4uPjow77+PgovXv3Vp577jllz5496rNGv/nmG8XR0VFJSkpS0tLSlLZt2yqbNm2qdT9vXm9ubq4CKO+9954SFhamtGrVSsnIyFDc3NyUAwcOKH379lXuv/9+5dChQ8r+/fuV++67T9m+fbuiKIry2WefKffff7+yd+9e5dixY8oDDzygxMTEqMsGlD/+8Y/K3/72N+Xrr79WysrKFEVRlMuXLyvp6ekKoFy6dEm5dOmSOu7LL79UmjVrphw9elTJzs5WOnbsqHz88ceKolx/Fu3999+vvPbaa0pubq7yxBNPKM8//7yiKIqyZcsWpXXr1so333yjnDp1SunUqZMay4YNGxSdTqccPHhQ2b9/v+Li4qLs37+/1jkTQiiKHIoVQtxzOnbsSHJysjrctm1bAD7++GO6du1KeHg4ANOmTeODDz5g7ty5dOjQgaKiIpydnTl8+DBms1ndi+Tm5qYuy93dvc6HeZs3b86mTZss2jZt2sTIkSMZNmwYAM8//zxJSUk899xzde7vjcLDw7lw4QL9+/enS5cutGzZUt3jNWXKFHr37g3AuHHjSExMZOTIkaxZs4aoqCgef/xx4PrewIULFxIVFaUut0uXLrz77rtV+uXu7g5QJSd9+/YlPz8fRVH49ttvASz2ynl7e7No0SIAxowZw9atWwFYv349UVFR/OlPfwLgww8/pKysTM3ZlClTCAoKAmDYsGEkJSXxP//zP78rZ0I0JVLYCSHuOU5OTlYvcCgoKOD48eNqEVJWVqZeVPHrr78yefJk9u/fz8MPP4y9vb166LAurl27VqVt+vTpVmPZu3evGktpaSmBgYF1Xt/NKs+9s3YOnre3t/rZ09OTH374AYAzZ87Qvn17dVyHDh3Iz8+3mHfGjBl1iuPChQuEhYWRmZlJ9+7dcXZ2tshn37591c9OTk7qYeibY3nkkUfUzwUFBRw8eJDVq1cD8N///peQkJA6xSVEUyeFnRCi0fDy8mLYsGEsW7YMgPLycrUQW7FiBVeuXOHnn3/G0dGRV155hXPnzlnMr9FoqpwHp9FoLM5HO3bsWJX1urq6Wo1l6tSp/O1vfwPAbDbf9atcT58+rX4+c+YMHh4eALRr146cnBx1XE5ODj4+PhbzWusDgJ3d9VOxFUVRz0MEmD9/Pt7e3nz99ddoNBpGjx5tMV/lnr6beXt7W8T54YcfsnfvXuLi4vDy8mLSpEk8/fTTAJhMJpycnG7RayHEjeTiCSFEozFmzBj279/Pjz/+iFar5b333mPixIkAFBcXU1FRwfnz59myZQurVq2qUsR16NCBnTt3cvbsWfbt2wdc3/N17NgxzGYz2dnZ6t6kWxk/fjyJiYkUFRXh4ODA66+/zuuvv35nO3yTuLg4Dh06xIEDB9iyZQujRo0CYPLkySxfvpxvvvmG48ePM3/+fF544YVaLbNNmza4uLjwxRdfkJeXp148UVxcTHl5OUVFRfy///f/2L59e60uDpk0aRLLly/nwIEDfP/990RHR9OhQwfges4++ugjiouLURSFKVOmEBsbe5vZEKJpksJOCNFodOjQgY0bN/LSSy/xwAMPkJGRoZ7bFRUVhclk4sEHH2T9+vX85S9/IS0tzWL+VatWsWzZMvz8/NQCbuzYsXh6etKxY0cmTpzIvHnzahXLY489xoIFC3j++ecxGAyUlpaycuXKO9rfm40ePZpnn32WkSNHEhUVxVNPPQXAqFGjeOONNxg/fjzBwcGMGzfO6uFjaxwdHVmzZg1Tp06lY8eOJCYmAjBv3jwyMjLo1KkTqampjBkzhuPHj99yeWPGjOGVV15h7NixPPbYYzz++OPMnDkTgOeee45nnnmGoUOH0q1bN/z8/HjrrbduMxtCNE0apTb/xRJCCNGgPf7444SFhcnTKoRo4mSPnRBCCCFEIyF77IQQQgghGgnZYyeEEEII0UhIYSeEEEII0UhIYSeEEEII0UhIYSeEEEII0UhIYSeEEEII0UhIYSeEEEII0UhIYSeEEEII0UhIYSeEEEII0Uj8fy3BWbdzna37AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# 准备数据集\n",
    "def prepare_data_for_lgbm(df, seq_len, target_col=\"Close\"):\n",
    "    \"\"\"\n",
    "    为LightGBM准备时序特征数据\n",
    "    \"\"\"\n",
    "    data = df.copy()\n",
    "\n",
    "    # 确保数据按日期排序\n",
    "    if \"Date\" in data.columns:\n",
    "        data = data.sort_values(\"Date\").reset_index(drop=True)\n",
    "\n",
    "    # 保留原始价格\n",
    "    original_prices = data[target_col].values\n",
    "\n",
    "    # 准备特征和目标\n",
    "    X, y = [], []\n",
    "\n",
    "    # 特征列（排除Date和目标列）\n",
    "    feature_cols = [col for col in data.columns if col != \"Date\" and col != target_col]\n",
    "\n",
    "    for i in range(len(data) - seq_len):\n",
    "        # 提取序列特征\n",
    "        feature_sequence = data.iloc[i : i + seq_len][feature_cols].values\n",
    "        # 展平特征序列\n",
    "        flat_features = feature_sequence.reshape(-1)\n",
    "        # 添加序列中的时间步特征\n",
    "        X.append(flat_features)\n",
    "        # 使用序列后的下一个值作为目标\n",
    "        y.append(original_prices[i + seq_len])\n",
    "\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "\n",
    "# 准备LightGBM数据集\n",
    "X_train, y_train = prepare_data_for_lgbm(df, seq_len)\n",
    "\n",
    "# 训练集和验证集划分\n",
    "train_size = int(len(X_train) * 0.8)\n",
    "X_valid, y_valid = X_train[train_size:], y_train[train_size:]\n",
    "X_train, y_train = X_train[:train_size], y_train[:train_size]\n",
    "\n",
    "# 创建LightGBM数据集\n",
    "train_data = lgb.Dataset(X_train, label=y_train)\n",
    "valid_data = lgb.Dataset(X_valid, label=y_valid, reference=train_data)\n",
    "\n",
    "# LightGBM参数\n",
    "params = {\n",
    "    \"objective\": \"regression\",\n",
    "    \"metric\": \"rmse\",\n",
    "    \"boosting_type\": \"gbdt\",\n",
    "    \"num_leaves\": 31,\n",
    "    \"learning_rate\": 0.05,\n",
    "    \"feature_fraction\": 0.9,\n",
    "    \"bagging_fraction\": 0.8,\n",
    "    \"bagging_freq\": 5,\n",
    "    \"verbose\": -1,\n",
    "}\n",
    "\n",
    "# 训练模型\n",
    "print(\"开始训练LightGBM模型...\")\n",
    "lgb_model = lgb.train(\n",
    "    params,\n",
    "    train_data,\n",
    "    num_boost_round=1000,\n",
    "    valid_sets=[valid_data],\n",
    ")\n",
    "\n",
    "# 保存模型\n",
    "lgb_model_path = os.path.join(MODEL_DIR, f\"lightgbm_model_{stock}.txt\")\n",
    "lgb_model.save_model(lgb_model_path)\n",
    "print(f\"保存LightGBM模型到 {lgb_model_path}\")\n",
    "\n",
    "# 使用LightGBM模型进行预测\n",
    "X_all, y_all = prepare_data_for_lgbm(df, seq_len)\n",
    "lgb_predictions = lgb_model.predict(X_all)\n",
    "\n",
    "# 计算LightGBM的评估指标\n",
    "lgb_rmse = np.sqrt(mean_squared_error(y_all, lgb_predictions))\n",
    "lgb_mae = mean_absolute_error(y_all, lgb_predictions)\n",
    "lgb_mape = np.mean(np.abs((y_all - lgb_predictions) / y_all)) * 100\n",
    "\n",
    "# 获取日期以便绘图\n",
    "dates = df.iloc[seq_len : len(lgb_predictions) + seq_len][\"Date\"].values\n",
    "\n",
    "# 打印对比评估指标\n",
    "print(\"\\n模型性能对比:\")\n",
    "print(f\"{'指标':<10} {'IndRNN-LSTM':<15} {'LightGBM':<15}\")\n",
    "print(\"-\" * 40)\n",
    "print(f\"{'RMSE':<10} {rmse:<15.2f} {lgb_rmse:<15.2f}\")\n",
    "print(f\"{'MAE':<10} {mae:<15.2f} {lgb_mae:<15.2f}\")\n",
    "print(f\"{'MAPE':<10} {mape:<15.2f}% {lgb_mape:<15.2f}%\")\n",
    "\n",
    "# 绘制比较图\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# 绘制实际值、IndRNN-LSTM和LightGBM的预测值\n",
    "plt.plot(dates, y_all, label=\"实际收盘价\", color=\"blue\", alpha=0.8)\n",
    "plt.plot(dates, predictions, label=\"IndRNN-LSTM预测\", color=\"red\", alpha=0.6)\n",
    "plt.plot(dates, lgb_predictions, label=\"LightGBM预测\", color=\"green\", alpha=0.6)\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title(f\"股票 {stock} 收盘价预测模型对比\", fontsize=16)\n",
    "plt.xlabel(\"日期\")\n",
    "plt.ylabel(\"股价\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "\n",
    "# 添加网格\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.5)\n",
    "\n",
    "# 显示评估指标\n",
    "plt.figtext(\n",
    "    0.15,\n",
    "    0.85,\n",
    "    f\"IndRNN-LSTM:\\nRMSE: {rmse:.2f}\\nMAE: {mae:.2f}\\nMAPE: {mape:.2f}%\\n\\n\"\n",
    "    f\"LightGBM:\\nRMSE: {lgb_rmse:.2f}\\nMAE: {lgb_mae:.2f}\\nMAPE: {lgb_mape:.2f}%\",\n",
    "    bbox=dict(facecolor=\"white\", alpha=0.8),\n",
    ")\n",
    "\n",
    "# 保存图像\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(OUTPUT_DIR, f\"stock_{stock}_model_comparison.png\"), dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# 显示LightGBM的特征重要性\n",
    "plt.figure(figsize=(12, 8))\n",
    "lgb.plot_importance(lgb_model, max_num_features=20)\n",
    "plt.title(f\"LightGBM特征重要性 - 股票{stock}\", fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(OUTPUT_DIR, f\"stock_{stock}_feature_importance.png\"), dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a71f511d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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